Dietary intake, Nutritional status, and Health outcomes among Vegan, Vegetarian and Omnivore families: results from the observational study
Statistical report - mixed-effects models in all children
Authors and affiliations
Marina Heniková1,2, Anna Ouřadová1, Eliška Selinger1,3, Filip Tichanek4, Petra Polakovičová4, Dana Hrnčířová2, Pavel Dlouhý2, Martin Světnička5, Eva El-Lababidi5, Jana Potočková1, Tilman Kühn6, Monika Cahová4, Jan Gojda1
1 Department of Internal Medicine, Kralovske Vinohrady University Hospital and Third Faculty of Medicine, Charles University, Prague, Czech Republic.
2 Department of Hygiene, Third Faculty of Medicine, Charles University, Prague, Czech Republic.
3 National Health Institute, Prague, Czech Republic.
4 Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
5 Department of Pediatrics, Kralovske Vinohrady University Hospital and Third Faculty of Medicine, Charles University, Prague, Czech Republic.
6 Department of Epidemiology, MedUni, Vienna, Austria.
This is a statistical report of the study currenlty under review in the Communications Medicine journal.
When using this code or data, cite the original publication:
TO BE ADDED
BibTex citation for the original publication:
TO BE ADDED
Original GitHub repository: https://github.com/filip-tichanek/kompas_clinical
Statistical reports can be found on the reports hub.
Data analysis is described in detail in the statistical methods report.
1 Introduction
This project is designed to evaluate and compare clinical outcomes across three distinct dietary strategy groups:
- Vegans
- Vegetarians
- Omnivores
The dataset includes both adults and children, with data clustered within families.
1.1 Main Questions
The study addresses the following key questions:
Q1. Do clinical outcomes vary significantly across different diet strategies?
Q2. Beyond diet group, which factors (e.g., sex, age, breastfeeding status for children, or supplementation when applicable) most strongly influence clinical outcomes? How correlated (“clustered”) are these characteristics within the same family?
Q3. Could the clinical characteristics effectively discriminate between different diet groups?
1.2 Statistical Methods
For full methodological details, see this report. In brief:
Robust linear mixed-effects models (rLME) were used to estimate adjusted differences between diet groups (Q1) and assess the importance of other variables (Q2), including how much clinical characteristics tend to cluster within families. Covariates included age, sex, breastfeeding status for children, and relevant supplementation factors where applicable.
Elastic net logistic regression was employed to answer Q3, evaluating whether clinical characteristics provide a strong overall signal distinguishing between diet groups, incorporating a predictive perspective.
All analyses were conducted separately for adults and children.
2 Analysis
2.1 Import initiation file
Open code
getwd()
## [1] "/home/ticf/GitRepo/ticf/368_MOCA_kompas_clinical"
setwd('/home/ticf/GitRepo/ticf/368_MOCA_kompas_clinical/')
source('r/368_initiation.R')2.2 Mixed models
All children models will be adjusted for the effect of log2_age (when applicable, i.e. not for age-adjusted Anthropometric data), SEX, aBreastFeed_full_stopped, aBreastFeed_total_stopped, and aBreastFeed_full_duration (the interaction of the latter with duration of the dependence on the breastfeeding). Additional covariates (relevant supplementation for vitamins and biogenic elements levels) or birth weight (anthropometric data) will be added as covariates.
Open code
AIC_child_all <- data.frame(outcome = NA,
estimand = 'mean',
log2_age = NA,
aSEX = NA,
Breast_Feed = NA,
other_cov = NA,
diet = NA,
family = NA)
diet_child_all <- data.frame(outcome = NA,
estimand = NA,
VN_OM_diff = NA,
VN_OM_P = NA,
VG_OM_diff = NA,
VG_OM_P = NA,
VN_VG_diff = NA,
VN_VG_P = NA)
diet_child_all_non_robust <- data.frame(outcome = NA,
estimand = NA,
VN_OM_diff = NA,
VN_OM_P = NA,
VG_OM_diff = NA,
VG_OM_P = NA,
VN_VG_diff = NA,
VN_VG_P = NA)
i = 1
j = 1
ni <- 102.2.1 aMASS_Perc
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aBiW))
column_name
## [1] "aMASS_Perc"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aBiW +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.700959 17.260547 -0.736 0.464
## aBreastFeed_full_stopped -11.910925 10.726816 -1.110 0.270
## aBreastFeed_full_duration -0.598803 1.059977 -0.565 0.573
## aBreastFeed_total_stopped 6.307452 5.067418 1.245 0.216
## SEXM -2.563793 4.216188 -0.608 0.545
## GRPVG -3.468036 6.590112 -0.526 0.600
## GRPVN -9.035730 5.717512 -1.580 0.117
## aBiW 0.022480 0.004126 5.448 3.97e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 36.66 92 0.756 0.00126 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.484 Deviance explained = 64.6%
## GCV = 631.04 Scale est. = 429.73 n = 140
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.233 0.270
## aBreastFeed_full_duration 1 0.319 0.573
## aBreastFeed_total_stopped 1 1.549 0.216
## SEX 1 0.370 0.545
## GRP 2 1.331 0.269
## aBiW 1 29.685 3.97e-07
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 36.66 92.00 0.756 0.00126
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, exclude = c('log2_age'),
include = c('aBiW'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -13.60584290 19.171806811 -0.7096797
## GRPVG -3.59757121 7.133137456 -0.5043463
## GRPVN -9.23123721 6.205851123 -1.4875054
## SEXM -2.38696398 4.702374700 -0.5076082
## aBreastFeed_full_stopped -15.89580812 12.000091190 -1.3246406
## aBreastFeed_full_duration -0.14178377 1.184237495 -0.1197258
## aBreastFeed_total_stopped 7.00262159 5.641911066 1.2411790
## aBiW 0.02290339 0.004569268 5.0124847
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of age-standardized percentile of body mass. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -9.231237 | -21.39448 | 2.932008 | 0.1368814 |
| VG vs OM | -3.597571 | -17.57826 | 10.383121 | 0.6140181 |
| VN vs VG | -5.633666 | -18.20819 | 6.940856 | 0.3798854 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(exclude = c('log2_age'),
include = c('aBiW'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme( exclude = c('log2_age'),
include = c('aBiW'), remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('log2_age', 'SEX'), include = 'aBiW')
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('log2_age',
'aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'),
include = 'aBiW')
### model without diet groups
mod_ndiet <- rlme(exclude = c('log2_age', 'GRP'), include = 'aBiW')
### model without birth weight
mod_other_cov <- rlme(exclude = c('log2_age'))
### model without log2_age
mod_log2_age <- NAPutting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE)
diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.347
## Unadjusted ICC: 0.266
i = i+12.2.2 aHEIGHT_Perc
Data selection and diagnostic plots
Open code
## Data selection and diagnostic plots
ni <- 10
column_name <- names(dat_child_all)[i+ni]
column_name
## [1] "aHEIGHT_Perc"
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aBiW))
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aBiW +
#s(log2_age)
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.81188 16.73962 -0.228 0.820
## aBreastFeed_full_stopped -11.41481 10.26311 -1.112 0.269
## aBreastFeed_full_duration 0.23064 1.01420 0.227 0.821
## aBreastFeed_total_stopped 0.95070 4.89853 0.194 0.847
## SEXM -0.34825 4.05298 -0.086 0.932
## GRPVG 0.02824 6.72575 0.004 0.997
## GRPVN -8.70175 5.81139 -1.497 0.138
## aBiW 0.01854 0.00402 4.611 1.4e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 47.21 92 1.208 4.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.543 Deviance explained = 72.1%
## GCV = 576.9 Scale est. = 349.4 n = 140
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.237 0.269
## aBreastFeed_full_duration 1 0.052 0.821
## aBreastFeed_total_stopped 1 0.038 0.847
## SEX 1 0.007 0.932
## GRP 2 1.617 0.205
## aBiW 1 21.264 1.4e-05
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 47.21 92.00 1.208 4.97e-05
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, exclude = c('log2_age'),
include = c('aBiW'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -4.990866084 18.016806377 -0.277011696
## GRPVG 0.858816421 7.718147703 0.111272348
## GRPVN -7.969334744 6.647194041 -1.198902077
## SEXM -0.163256291 4.309701831 -0.037881110
## aBreastFeed_full_stopped -9.085867623 10.871090074 -0.835782572
## aBreastFeed_full_duration -0.008276443 1.070600889 -0.007730652
## aBreastFeed_total_stopped 1.170957605 5.259674358 0.222629297
## aBiW 0.018461899 0.004343767 4.250204501
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects regression modelling of age-standardized percentile of body height. CI_L and CI_U are bounds of 95% confidence interval. Estimates are based on bootstrap (5000 simulations)') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1.5in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -7.9693347 | -20.99760 | 5.058926 | 0.2305660 |
| VG vs OM | 0.8588164 | -14.26848 | 15.986108 | 0.9114004 |
| VN vs VG | -8.8281512 | -22.23353 | 4.577226 | 0.1967938 |
Leave-one-factor lqm
Open code
### main model but non-robust
mod_main <- rlme(exclude = c('log2_age'),
include = c('aBiW'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme( exclude = c('log2_age'),
include = c('aBiW'), remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('log2_age', 'SEX'), include = 'aBiW')
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('log2_age',
'aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'),
include = 'aBiW')
### model without diet groups
mod_ndiet <- rlme(exclude = c('log2_age', 'GRP'), include = 'aBiW')
### model without birth weight
mod_other_cov <- rlme(exclude = c('log2_age'))
### model without log2_age
mod_log2_age <- NA
# rlme(exclude = c('log2_age'), include = 'aBiW')Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.459
## Unadjusted ICC: 0.374
i = i+12.2.3 aBMI_PERC
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aBiW))
column_name
## [1] "aBMI_PERC"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aBiW +
#s(log2_age)
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.581404 16.470294 0.764 0.446809
## aBreastFeed_full_stopped 2.269435 10.243692 0.222 0.825139
## aBreastFeed_full_duration -1.363984 1.012137 -1.348 0.180950
## aBreastFeed_total_stopped 2.703043 4.836547 0.559 0.577546
## SEXM -4.714809 4.025037 -1.171 0.244349
## GRPVG -2.937324 6.270471 -0.468 0.640534
## GRPVN -2.506189 5.441721 -0.461 0.646162
## aBiW 0.013496 0.003936 3.429 0.000895 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 36 92 0.699 0.00299 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.365 Deviance explained = 56.2%
## GCV = 575.48 Scale est. = 394.62 n = 140
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.049 0.825139
## aBreastFeed_full_duration 1 1.816 0.180950
## aBreastFeed_total_stopped 1 0.312 0.577546
## SEX 1 1.372 0.244349
## GRP 2 0.139 0.869990
## aBiW 1 11.757 0.000895
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 36 92 0.699 0.00299
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, exclude = c('log2_age'),
include = c('aBiW'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 8.421909 17.170011614 0.4905011
## GRPVG -2.790478 7.092862170 -0.3934206
## GRPVN -2.318842 6.118688518 -0.3789769
## SEXM -4.369637 4.135927817 -1.0565072
## aBreastFeed_full_stopped 8.083229 10.452991829 0.7732933
## aBreastFeed_full_duration -1.831574 1.031813714 -1.7751019
## aBreastFeed_total_stopped 1.722133 5.018471399 0.3431590
## aBiW 0.013933 0.004131799 3.3721383
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of age-standardized percentile of BMI. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -2.318842 | -14.31125 | 9.673567 | 0.7047050 |
| VG vs OM | -2.790478 | -16.69223 | 11.111277 | 0.6940089 |
| VN vs VG | 0.471636 | -11.87847 | 12.821746 | 0.9403351 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(exclude = c('log2_age'),
include = c('aBiW'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme( exclude = c('log2_age'),
include = c('aBiW'), remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('log2_age', 'SEX'), include = 'aBiW')
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('log2_age',
'aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'),
include = 'aBiW')
### model without diet groups
mod_ndiet <- rlme(exclude = c('log2_age', 'GRP'), include = 'aBiW')
### model without birth weight
mod_other_cov <- rlme(exclude = c('log2_age'))
### model without log2_age
mod_log2_age <- NAPutting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.318
## Unadjusted ICC: 0.284
i = i+12.2.4 aM_per_H_PERC
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aBiW))
column_name
## [1] "aM_per_H_PERC"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aBiW +
#s(log2_age)
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.77055 16.52976 0.289 0.773472
## aBreastFeed_full_stopped 11.29903 10.34650 1.092 0.277387
## aBreastFeed_full_duration -1.43693 1.02105 -1.407 0.162386
## aBreastFeed_total_stopped 1.77547 4.86443 0.365 0.715877
## SEXM -3.38832 4.05437 -0.836 0.405272
## GRPVG -3.39585 6.14985 -0.552 0.582034
## GRPVN -3.04783 5.35041 -0.570 0.570176
## aBiW 0.01345 0.00394 3.413 0.000922 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 30.32 92 0.531 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.308 Deviance explained = 49.4%
## GCV = 586.82 Scale est. = 426.18 n = 140
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aBiW + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.193 0.277387
## aBreastFeed_full_duration 1 1.981 0.162386
## aBreastFeed_total_stopped 1 0.133 0.715877
## SEX 1 0.698 0.405272
## GRP 2 0.204 0.815929
## aBiW 1 11.652 0.000922
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FAM) 30.32 92.00 0.531 0.0109
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, exclude = c('log2_age'),
include = c('aBiW'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.42752142 17.856789359 0.02394167
## GRPVG -3.66067251 6.472091557 -0.56560889
## GRPVN -3.16988489 5.650048603 -0.56103675
## SEXM -3.18334185 4.396888867 -0.72399871
## aBreastFeed_full_stopped 12.32997123 11.260511790 1.09497432
## aBreastFeed_full_duration -1.28144002 1.108615210 -1.15589251
## aBreastFeed_total_stopped 1.79114874 5.270951121 0.33981509
## aBiW 0.01421465 0.004241191 3.35157052
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of age-standardized percentile of weight to height ratio. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -3.1698849 | -14.24378 | 7.904007 | 0.5747725 |
| VG vs OM | -3.6606725 | -16.34574 | 9.024394 | 0.5716597 |
| VN vs VG | 0.4907876 | -10.97281 | 11.954388 | 0.9331269 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(exclude = c('log2_age'),
include = c('aBiW'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme( exclude = c('log2_age'),
include = c('aBiW'), remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('log2_age', 'SEX'), include = 'aBiW')
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('log2_age',
'aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'),
include = 'aBiW')
### model without diet groups
mod_ndiet <- rlme(exclude = c('log2_age', 'GRP'), include = 'aBiW')
### model without birth weight
mod_other_cov <- rlme(exclude = c('log2_age'))
### model without log2_age
mod_log2_age <- NAPutting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.259
## Unadjusted ICC: 0.233
i = i+12.2.5 aGLY
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aGLY"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.53487 0.30614 14.813 <2e-16 ***
## aBreastFeed_full_stopped 0.06490 0.31825 0.204 0.839
## aBreastFeed_full_duration -0.02608 0.02613 -0.998 0.320
## aBreastFeed_total_stopped -0.02682 0.15763 -0.170 0.865
## SEXM 0.06084 0.10517 0.579 0.564
## GRPVG 0.01676 0.15147 0.111 0.912
## GRPVN -0.03333 0.13080 -0.255 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1 0.030 0.864
## s(FAM) 12.9 87 0.182 0.159
##
## R-sq.(adj) = 0.0775 Deviance explained = 21.8%
## GCV = 0.37835 Scale est. = 0.31845 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.042 0.839
## aBreastFeed_full_duration 1 0.996 0.320
## aBreastFeed_total_stopped 1 0.029 0.865
## SEX 1 0.335 0.564
## GRP 2 0.070 0.933
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1.0 0.030 0.864
## s(FAM) 12.9 87.0 0.182 0.159
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.168629 0.093955 23.082 <2e-16 ***
## aBreastFeed_full_stopped 0.018032 0.097713 0.185 0.854
## aBreastFeed_full_duration -0.008025 0.008018 -1.001 0.319
## aBreastFeed_total_stopped -0.014262 0.048375 -0.295 0.769
## SEXM 0.026719 0.032279 0.828 0.410
## GRPVG 0.006062 0.046325 0.131 0.896
## GRPVN -0.006405 0.040026 -0.160 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 0.053 0.818
## s(FAM) 11.97 87 0.165 0.185
##
## R-sq.(adj) = 0.0718 Deviance explained = 20.6%
## GCV = 0.035654 Scale est. = 0.030261 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.034 0.854
## aBreastFeed_full_duration 1 1.002 0.319
## aBreastFeed_total_stopped 1 0.087 0.769
## SEX 1 0.685 0.410
## GRP 2 0.042 0.959
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 0.053 0.818
## s(FAM) 11.97 87.00 0.165 0.185
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 4.503637802 0.21008595 21.4371204
## GRPVG 0.051518564 0.11291295 0.4562680
## GRPVN 0.009939696 0.09840296 0.1010101
## SEXM 0.083487939 0.08214344 1.0163677
## aBreastFeed_full_stopped 0.041847382 0.24976969 0.1675439
## aBreastFeed_full_duration -0.017282491 0.02036016 -0.8488388
## aBreastFeed_total_stopped -0.089759485 0.12301474 -0.7296645
## log2_age -0.022650928 0.05373160 -0.4215570
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of glycemia. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0099397 | -0.1829266 | 0.2028060 | 0.9195424 |
| VG vs OM | 0.0515186 | -0.1697868 | 0.2728239 | 0.6481973 |
| VN vs VG | -0.0415789 | -0.2528550 | 0.1696973 | 0.6997053 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.105
## Unadjusted ICC: 0.103
i = i+12.2.6 aTC
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aTC"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.86113 0.36676 10.528 <2e-16 ***
## aBreastFeed_full_stopped 0.21867 0.37496 0.583 0.5612
## aBreastFeed_full_duration 0.01796 0.02778 0.647 0.5195
## aBreastFeed_total_stopped -0.25101 0.17094 -1.468 0.1454
## SEXM -0.05079 0.11245 -0.452 0.6525
## GRPVG -0.19030 0.17308 -1.100 0.2744
## GRPVN -0.31981 0.14833 -2.156 0.0337 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.07 2.54 1.414 0.2166
## s(FAM) 30.15 87.00 0.566 0.0108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.313 Deviance explained = 51.3%
## GCV = 0.42491 Scale est. = 0.29867 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.340 0.561
## aBreastFeed_full_duration 1 0.418 0.519
## aBreastFeed_total_stopped 1 2.156 0.145
## SEX 1 0.204 0.653
## GRP 2 2.324 0.104
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.07 2.54 1.414 0.2166
## s(FAM) 30.15 87.00 0.566 0.0108
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 4.003671507 0.29977251 13.355699189
## GRPVG -0.182702640 0.17935394 -1.018670892
## GRPVN -0.320313125 0.15363806 -2.084855272
## SEXM -0.026242596 0.11688578 -0.224514872
## aBreastFeed_full_stopped -0.002126509 0.35168225 -0.006046676
## aBreastFeed_full_duration 0.021438364 0.02910158 0.736673645
## aBreastFeed_total_stopped -0.283445212 0.17608030 -1.609749706
## log2_age 0.035909877 0.07716968 0.465336582
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of total cholesterol. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.3203131 | -0.6214382 | -0.0191881 | 0.0370824 |
| VG vs OM | -0.1827026 | -0.5342299 | 0.1688246 | 0.3083592 |
| VN vs VG | -0.1376105 | -0.4642840 | 0.1890631 | 0.4090138 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.273
## Unadjusted ICC: 0.256
i = i+12.2.7 aHDL
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aHDL"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2514911 0.1736247 7.208 3.35e-10 ***
## aBreastFeed_full_stopped 0.0784785 0.1753776 0.447 0.6558
## aBreastFeed_full_duration 0.0006637 0.0131937 0.050 0.9600
## aBreastFeed_total_stopped -0.1263664 0.0823466 -1.535 0.1290
## SEXM 0.0930663 0.0534365 1.742 0.0856 .
## GRPVG -0.0716227 0.0905130 -0.791 0.4312
## GRPVN -0.0147399 0.0771207 -0.191 0.8489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.907 2.307 4.604 0.012109 *
## s(FAM) 46.117 87.000 1.217 0.000132 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.5 Deviance explained = 70.6%
## GCV = 0.095377 Scale est. = 0.055619 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.200 0.6558
## aBreastFeed_full_duration 1 0.003 0.9600
## aBreastFeed_total_stopped 1 2.355 0.1290
## SEX 1 3.033 0.0856
## GRP 2 0.347 0.7082
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.907 2.307 4.604 0.012109
## s(FAM) 46.117 87.000 1.217 0.000132
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.235071 0.200055 1.175 0.2437
## aBreastFeed_full_stopped 0.123274 0.202090 0.610 0.5437
## aBreastFeed_full_duration 0.000145 0.014669 0.010 0.9921
## aBreastFeed_total_stopped -0.149691 0.092340 -1.621 0.1092
## SEXM 0.103830 0.059581 1.743 0.0855 .
## GRPVG -0.054645 0.102429 -0.533 0.5953
## GRPVN 0.006416 0.087248 0.074 0.9416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.26 2.739 4.814 0.00724 **
## s(FAM) 48.35 87.000 1.388 3.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.536 Deviance explained = 73.7%
## GCV = 0.11818 Scale est. = 0.066604 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.372 0.5437
## aBreastFeed_full_duration 1 0.000 0.9921
## aBreastFeed_total_stopped 1 2.628 0.1092
## SEX 1 3.037 0.0855
## GRP 2 0.228 0.7970
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.260 2.739 4.814 0.00724
## s(FAM) 48.348 87.000 1.388 3.86e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Diagnostic plot looks better now, but the difference in not huge. We will use original values to unify it with LDL analysis where transformation is unsuitable.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.221828302 0.13135304 9.3018652
## GRPVG -0.062871348 0.09315387 -0.6749193
## GRPVN -0.011681354 0.07916009 -0.1475662
## SEXM 0.103468531 0.05047009 2.0500962
## aBreastFeed_full_stopped -0.062432570 0.14952516 -0.4175389
## aBreastFeed_full_duration 0.001456792 0.01255316 0.1160498
## aBreastFeed_total_stopped -0.134853396 0.07839031 -1.7202815
## log2_age 0.100586629 0.03344898 3.0071657
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of HDL cholesterol level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0116814 | -0.1668323 | 0.1434696 | 0.8826851 |
| VG vs OM | -0.0628713 | -0.2454496 | 0.1197069 | 0.4997270 |
| VN vs VG | 0.0511900 | -0.1146963 | 0.2170763 | 0.5453018 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.466
## Unadjusted ICC: 0.429
i = i+12.2.8 aLDL
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aLDL"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.92602 0.31511 6.112 3.04e-08 ***
## aBreastFeed_full_stopped 0.39351 0.32049 1.228 0.2230
## aBreastFeed_full_duration 0.01220 0.02351 0.519 0.6051
## aBreastFeed_total_stopped -0.20773 0.14604 -1.422 0.1586
## SEXM -0.07770 0.09530 -0.815 0.4172
## GRPVG -0.01018 0.15442 -0.066 0.9476
## GRPVN -0.30283 0.13186 -2.297 0.0242 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.17 2.643 0.754 0.51430
## s(FAM) 39.64 87.000 0.859 0.00206 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.404 Deviance explained = 62.1%
## GCV = 0.30373 Scale est. = 0.19143 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.508 0.223
## aBreastFeed_full_duration 1 0.269 0.605
## aBreastFeed_total_stopped 1 2.023 0.159
## SEX 1 0.665 0.417
## GRP 2 3.491 0.035
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.170 2.643 0.754 0.51430
## s(FAM) 39.637 87.000 0.859 0.00206
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.082642 0.299259 3.618 0.000597 ***
## aBreastFeed_full_stopped 0.203347 0.304221 0.668 0.506334
## aBreastFeed_full_duration 0.001904 0.019061 0.100 0.920751
## aBreastFeed_total_stopped -0.245693 0.124691 -1.970 0.053239 .
## SEXM -0.071477 0.078326 -0.913 0.364989
## GRPVG -0.036316 0.147856 -0.246 0.806786
## GRPVN -0.223078 0.125665 -1.775 0.080753 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 4.019 4.773 1.768 0.103
## s(FAM) 58.670 87.000 1.964 8.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.595 Deviance explained = 80.8%
## GCV = 0.20303 Scale est. = 0.09584 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.447 0.5063
## aBreastFeed_full_duration 1 0.010 0.9208
## aBreastFeed_total_stopped 1 3.883 0.0532
## SEX 1 0.833 0.3650
## GRP 2 1.908 0.1570
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 4.019 4.773 1.768 0.103
## s(FAM) 58.670 87.000 1.964 8.11e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.911899592 0.24645384 7.7576375
## GRPVG 0.078278763 0.13245927 0.5909648
## GRPVN -0.252304231 0.11543746 -2.1856357
## SEXM -0.046105284 0.09636326 -0.4784529
## aBreastFeed_full_stopped 0.369156136 0.29300722 1.2598875
## aBreastFeed_full_duration 0.007058201 0.02388470 0.2955114
## aBreastFeed_total_stopped -0.117545760 0.14430977 -0.8145378
## log2_age -0.038489757 0.06303305 -0.6106282
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of LDL cholesterol level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.2523042 | -0.4785575 | -0.0260510 | 0.0288423 |
| VG vs OM | 0.0782788 | -0.1813366 | 0.3378942 | 0.5545440 |
| VN vs VG | -0.3305830 | -0.5784331 | -0.0827329 | 0.0089436 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.322
## Unadjusted ICC: 0.293
i = i+12.2.9 aTG
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aTG"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6513536 0.2834306 5.826 8.96e-08 ***
## aBreastFeed_full_stopped -0.5959491 0.2909482 -2.048 0.0435 *
## aBreastFeed_full_duration 0.0008207 0.0241676 0.034 0.9730
## aBreastFeed_total_stopped 0.1657157 0.1468551 1.128 0.2622
## SEXM -0.1516153 0.0970188 -1.563 0.1217
## GRPVG -0.2480591 0.1546691 -1.604 0.1123
## GRPVN -0.0277352 0.1320841 -0.210 0.8342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 4.257 0.04201 *
## s(FAM) 35.26 87 0.754 0.00222 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.409 Deviance explained = 60%
## GCV = 0.31937 Scale est. = 0.2147 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 4.196 0.0435
## aBreastFeed_full_duration 1 0.001 0.9730
## aBreastFeed_total_stopped 1 1.273 0.2622
## SEX 1 2.442 0.1217
## GRP 2 1.542 0.2197
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 4.257 0.04201
## s(FAM) 35.26 87.00 0.754 0.00222
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.369053 0.329267 1.121 0.2654
## aBreastFeed_full_stopped -0.357689 0.337751 -1.059 0.2925
## aBreastFeed_full_duration -0.013942 0.028070 -0.497 0.6206
## aBreastFeed_total_stopped 0.176375 0.170698 1.033 0.3043
## SEXM -0.195188 0.112680 -1.732 0.0867 .
## GRPVG -0.310227 0.180657 -1.717 0.0895 .
## GRPVN -0.001684 0.154220 -0.011 0.9913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 4.595 0.034826 *
## s(FAM) 36.28 87 0.824 0.000942 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.426 Deviance explained = 61.6%
## GCV = 0.43062 Scale est. = 0.28616 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.122 0.2925
## aBreastFeed_full_duration 1 0.247 0.6206
## aBreastFeed_total_stopped 1 1.068 0.3043
## SEX 1 3.001 0.0867
## GRP 2 1.991 0.1427
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 4.595 0.034826
## s(FAM) 36.28 87.00 0.824 0.000942
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.693242766 0.28059330 2.4706320
## GRPVG -0.306006754 0.17961065 -1.7037227
## GRPVN -0.008325549 0.15309046 -0.0543832
## SEXM -0.201858851 0.10897611 -1.8523220
## aBreastFeed_full_stopped -0.425854562 0.32588729 -1.3067541
## aBreastFeed_full_duration -0.018184420 0.02714738 -0.6698406
## aBreastFeed_total_stopped 0.145466145 0.16578495 0.8774388
## log2_age -0.151948674 0.07211229 -2.1071120
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of TG cholesterol level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0083255 | -0.3083773 | 0.2917262 | 0.9566299 |
| VG vs OM | -0.3060068 | -0.6580372 | 0.0460237 | 0.0884329 |
| VN vs VG | 0.2976812 | -0.0258080 | 0.6211704 | 0.0712944 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.378
## Unadjusted ICC: 0.323
i = i+12.2.10 aCa
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aCa"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.624678 0.047059 55.774 <2e-16 ***
## aBreastFeed_full_stopped -0.014348 0.047525 -0.302 0.764
## aBreastFeed_full_duration -0.002326 0.003872 -0.601 0.550
## aBreastFeed_total_stopped -0.015607 0.023909 -0.653 0.516
## SEXM -0.021236 0.015535 -1.367 0.176
## GRPVG 0.009508 0.027247 0.349 0.728
## GRPVN -0.010115 0.023057 -0.439 0.662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.20 1.327 21.853 3.79e-06 ***
## s(FAM) 49.97 88.000 1.312 0.000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.657 Deviance explained = 80.6%
## GCV = 0.0081933 Scale est. = 0.0046099 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.091 0.764
## aBreastFeed_full_duration 1 0.361 0.550
## aBreastFeed_total_stopped 1 0.426 0.516
## SEX 1 1.869 0.176
## GRP 2 0.327 0.722
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.200 1.327 21.853 3.79e-06
## s(FAM) 49.968 88.000 1.312 0.000134
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.713934960 0.041768239 64.9760452
## GRPVG 0.007421540 0.028911488 0.2566986
## GRPVN -0.016722252 0.024445181 -0.6840715
## SEXM -0.019231319 0.016029566 -1.1997404
## aBreastFeed_full_stopped -0.019544360 0.047797391 -0.4089001
## aBreastFeed_full_duration -0.002265238 0.004002109 -0.5660112
## aBreastFeed_total_stopped -0.025071669 0.024749264 -1.0130269
## log2_age -0.054295935 0.010653890 -5.0963485
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of of serum Ca level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0167223 | -0.0646339 | 0.0311894 | 0.4939300 |
| VG vs OM | 0.0074215 | -0.0492439 | 0.0640870 | 0.7974114 |
| VN vs VG | -0.0241438 | -0.0756253 | 0.0273377 | 0.3579991 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(,remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Ca'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Ca'),
exclude = c('log2_age'))Putting key results together
Open code
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.461
## Unadjusted ICC: 0.291
i = i+12.2.11 aP
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aP"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.595472 0.073719 21.643 <2e-16 ***
## aBreastFeed_full_stopped 0.057788 0.075698 0.763 0.447
## aBreastFeed_full_duration -0.002964 0.006282 -0.472 0.638
## aBreastFeed_total_stopped 0.060870 0.038009 1.601 0.113
## SEXM -0.004426 0.025120 -0.176 0.861
## GRPVG -0.005682 0.040220 -0.141 0.888
## GRPVN -0.029373 0.034163 -0.860 0.392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 39.40 < 2e-16 ***
## s(FAM) 35.57 88 0.78 0.00128 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.528 Deviance explained = 68%
## GCV = 0.021628 Scale est. = 0.014543 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.583 0.447
## aBreastFeed_full_duration 1 0.223 0.638
## aBreastFeed_total_stopped 1 2.565 0.113
## SEX 1 0.031 0.861
## GRP 2 0.431 0.651
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 39.40 < 2e-16
## s(FAM) 35.57 88.00 0.78 0.00128
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.736445491 0.070424320 24.65690107
## GRPVG 0.003339893 0.045057853 0.07412454
## GRPVN -0.027644335 0.038198440 -0.72370323
## SEXM -0.004288312 0.027220581 -0.15753934
## aBreastFeed_full_stopped 0.059597041 0.081784447 0.72870874
## aBreastFeed_full_duration -0.002388312 0.006806509 -0.35088655
## aBreastFeed_total_stopped 0.070362794 0.041391529 1.69993224
## log2_age -0.109035378 0.018074546 -6.03253759
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of serum P level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0276443 | -0.1025119 | 0.0472232 | 0.4692479 |
| VG vs OM | 0.0033399 | -0.0849719 | 0.0916517 | 0.9409113 |
| VN vs VG | -0.0309842 | -0.1119776 | 0.0500091 | 0.4533815 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.365
## Unadjusted ICC: 0.252
i = i+12.2.12 aMg
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Mg))
column_name
## [1] "aMg"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Mg +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Mg + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.878568 0.033199 26.464 <2e-16 ***
## aBreastFeed_full_stopped 0.011782 0.033802 0.349 0.7284
## aBreastFeed_full_duration -0.004623 0.002812 -1.644 0.1041
## aBreastFeed_total_stopped -0.029286 0.017234 -1.699 0.0932 .
## SEXM 0.010022 0.011258 0.890 0.3761
## GRPVG 0.028243 0.019284 1.465 0.1470
## GRPVN 0.028223 0.016349 1.726 0.0883 .
## aSUP_Mg -0.029670 0.031203 -0.951 0.3446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 2.837 0.09614 .
## s(FAM) 45.82 88 1.039 0.00116 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.517 Deviance explained = 71.4%
## GCV = 0.0043252 Scale est. = 0.0025426 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Mg + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.121 0.7284
## aBreastFeed_full_duration 1 2.704 0.1041
## aBreastFeed_total_stopped 1 2.888 0.0932
## SEX 1 0.792 0.3761
## GRP 2 1.703 0.1887
## aSUP_Mg 1 0.904 0.3446
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 2.837 0.09614
## s(FAM) 45.82 88.00 1.039 0.00116
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Mg'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.870776459 0.028291003 30.7792712
## GRPVG 0.025967756 0.015360512 1.6905527
## GRPVN 0.028799596 0.013298962 2.1655522
## SEXM 0.004882369 0.011016502 0.4431869
## aBreastFeed_full_stopped 0.030128945 0.033718439 0.8935451
## aBreastFeed_full_duration -0.003989540 0.002740525 -1.4557576
## aBreastFeed_total_stopped -0.028843504 0.016589529 -1.7386571
## log2_age -0.012765641 0.007535495 -1.6940679
## aSUP_Mg -0.016825712 0.026210038 -0.6419568
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of of serum Mg level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0287996 | 0.0027341 | 0.0548651 | 0.0303454 |
| VG vs OM | 0.0259678 | -0.0041383 | 0.0560738 | 0.0909223 |
| VN vs VG | 0.0028318 | -0.0255770 | 0.0312407 | 0.8451016 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Mg'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Mg'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Mg'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Mg'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Mg'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Mg'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.345
## Unadjusted ICC: 0.269
i = i+12.2.13 aSe
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Se))
column_name
## [1] "aSe"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Se +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.594086 0.171839 3.457 0.000929 ***
## aBreastFeed_full_stopped 0.288464 0.176778 1.632 0.107185
## aBreastFeed_full_duration -0.014666 0.011253 -1.303 0.196707
## aBreastFeed_total_stopped 0.005528 0.071658 0.077 0.938724
## SEXM 0.016765 0.046002 0.364 0.716612
## GRPVG 0.019396 0.081101 0.239 0.811673
## GRPVN -0.058158 0.068464 -0.849 0.398492
## aSUP_Se 0.437721 0.194520 2.250 0.027556 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 3.434 4.118 0.727 0.649
## s(FAM) 49.096 85.000 1.423 6.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.513 Deviance explained = 73.6%
## GCV = 0.069977 Scale est. = 0.037643 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 2.663 0.1072
## aBreastFeed_full_duration 1 1.699 0.1967
## aBreastFeed_total_stopped 1 0.006 0.9387
## SEX 1 0.133 0.7166
## GRP 2 0.655 0.5226
## aSUP_Se 1 5.064 0.0276
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 3.434 4.118 0.727 0.649
## s(FAM) 49.096 85.000 1.423 6.68e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Se +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.794784 0.310834 -2.557 0.0132 *
## aBreastFeed_full_stopped 0.570054 0.316266 1.802 0.0767 .
## aBreastFeed_full_duration -0.027189 0.018976 -1.433 0.1573
## aBreastFeed_total_stopped 0.002423 0.125417 0.019 0.9847
## SEXM 0.029882 0.078284 0.382 0.7041
## GRPVG -0.010152 0.156673 -0.065 0.9486
## GRPVN -0.148889 0.132074 -1.127 0.2643
## aSUP_Se 0.762199 0.375616 2.029 0.0470 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 4.102 4.874 1.375 0.349
## s(FAM) 60.933 85.000 2.654 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.663 Deviance explained = 85%
## GCV = 0.20133 Scale est. = 0.089087 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 3.249 0.0767
## aBreastFeed_full_duration 1 2.053 0.1573
## aBreastFeed_total_stopped 1 0.000 0.9847
## SEX 1 0.146 0.7041
## GRP 2 0.811 0.4494
## aSUP_Se 1 4.118 0.0470
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 4.102 4.874 1.375 0.349
## s(FAM) 60.933 85.000 2.654 <2e-16
plot(gamm, select = 1)
Open code
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.345
## Unadjusted ICC: 0.269
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The distribution of residuals seem a little bit better. Lets continue continue on log-scale
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Se'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -0.644653891 0.18240050 -3.53427706
## GRPVG -0.015944069 0.15383933 -0.10364105
## GRPVN -0.168973335 0.13004310 -1.29936412
## SEXM -0.014480457 0.06752899 -0.21443320
## aBreastFeed_full_stopped 0.471299911 0.19853369 2.37390392
## aBreastFeed_full_duration -0.029831051 0.01675827 -1.78007934
## aBreastFeed_total_stopped 0.006874390 0.10909572 0.06301247
## log2_age -0.004513157 0.04539399 -0.09942188
## aSUP_Se 0.783071123 0.36996676 2.11659856
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(serum Se level). CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1689733 | -0.4238531 | 0.0859065 | 0.1938190 |
| VG vs OM | -0.0159441 | -0.3174636 | 0.2855755 | 0.9174542 |
| VN vs VG | -0.1530293 | -0.4286581 | 0.1225996 | 0.2765187 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Se'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Se'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Se'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Se'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Se'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Se'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.622
## Unadjusted ICC: 0.576
i = i+12.2.14 aZn
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Zn))
column_name
## [1] "aZn"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Zn +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Zn + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.91857 1.23740 9.632 1.08e-15 ***
## aBreastFeed_full_stopped 0.41895 1.28687 0.326 0.745
## aBreastFeed_full_duration -0.01012 0.10662 -0.095 0.925
## aBreastFeed_total_stopped -0.27291 0.63905 -0.427 0.670
## SEXM 0.03881 0.41963 0.092 0.927
## GRPVG -0.61041 0.65565 -0.931 0.354
## GRPVN -0.85555 0.55998 -1.528 0.130
## aSUP_Zn -0.30574 1.26267 -0.242 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 1.367 0.2452
## s(FAM) 27.65 85 0.523 0.0138 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.257 Deviance explained = 46.1%
## GCV = 5.998 Scale est. = 4.3199 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Zn + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.106 0.745
## aBreastFeed_full_duration 1 0.009 0.925
## aBreastFeed_total_stopped 1 0.182 0.670
## SEX 1 0.009 0.927
## GRP 2 1.197 0.307
## aSUP_Zn 1 0.059 0.809
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 1.367 0.2452
## s(FAM) 27.65 85.00 0.523 0.0138
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Se +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5429675 0.1484454 23.867 <2e-16 ***
## aBreastFeed_full_stopped 0.0541757 0.1534889 0.353 0.725
## aBreastFeed_full_duration 0.0002158 0.0126196 0.017 0.986
## aBreastFeed_total_stopped -0.0448395 0.0762395 -0.588 0.558
## SEXM -0.0009393 0.0505791 -0.019 0.985
## GRPVG -0.0641195 0.0800983 -0.801 0.426
## GRPVN -0.1099749 0.0680649 -1.616 0.110
## aSUP_Se 0.0044955 0.1935471 0.023 0.982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 1.273 0.26211
## s(FAM) 31.01 85 0.633 0.00574 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.297 Deviance explained = 50.8%
## GCV = 0.087011 Scale est. = 0.060437 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.125 0.725
## aBreastFeed_full_duration 1 0.000 0.986
## aBreastFeed_total_stopped 1 0.346 0.558
## SEX 1 0.000 0.985
## GRP 2 1.305 0.276
## aSUP_Se 1 0.001 0.982
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 1.273 0.26211
## s(FAM) 31.01 85.00 0.633 0.00574
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The distribution of residuals seem better. Lets continue continue on log-scale
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Zn'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.515993174 0.11505756 30.55855956
## GRPVG -0.081308430 0.07593308 -1.07079066
## GRPVN -0.127464488 0.06450398 -1.97607183
## SEXM -0.001046543 0.04459465 -0.02346791
## aBreastFeed_full_stopped 0.026217017 0.13559335 0.19335032
## aBreastFeed_full_duration 0.002716316 0.01131953 0.23996733
## aBreastFeed_total_stopped -0.060183499 0.06881149 -0.87461404
## log2_age 0.045844183 0.03062325 1.49703855
## aSUP_Zn -0.040047731 0.14282205 -0.28040300
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of serum Zn level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1274645 | -0.2538900 | -0.0010390 | 0.0481466 |
| VG vs OM | -0.0813084 | -0.2301345 | 0.0675177 | 0.2842636 |
| VN vs VG | -0.0461561 | -0.1876719 | 0.0953598 | 0.5226594 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Zn'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Zn'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Zn'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Zn'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Zn'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Zn'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.299
## Unadjusted ICC: 0.285
i = i+12.2.15 aFE
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aFE"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.8736 3.4412 4.032 0.000113 ***
## aBreastFeed_full_stopped -2.0134 3.4728 -0.580 0.563482
## aBreastFeed_full_duration 0.1975 0.2830 0.698 0.486995
## aBreastFeed_total_stopped 0.2402 1.7515 0.137 0.891225
## SEXM -0.6136 1.1312 -0.542 0.588854
## GRPVG -0.6106 1.7671 -0.346 0.730480
## GRPVN 1.4615 1.5049 0.971 0.333999
## aSUP_Fe -0.3420 2.3990 -0.143 0.886946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.076 1.134 7.945 0.00454 **
## s(FAM) 30.643 88.000 0.591 0.00690 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.356 Deviance explained = 54.5%
## GCV = 44.2 Scale est. = 31 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.336 0.563
## aBreastFeed_full_duration 1 0.487 0.487
## aBreastFeed_total_stopped 1 0.019 0.891
## SEX 1 0.294 0.589
## GRP 2 0.948 0.391
## aSUP_Fe 1 0.020 0.887
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.076 1.134 7.945 0.00454
## s(FAM) 30.643 88.000 0.591 0.00690
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Fe'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 10.7708087 2.9914062 3.60058384
## GRPVG -0.7739504 1.5835387 -0.48874736
## GRPVN 1.8052234 1.3738149 1.31402229
## SEXM -1.1644473 1.1466696 -1.01550374
## aBreastFeed_full_stopped -2.1540705 3.5264332 -0.61083549
## aBreastFeed_full_duration 0.1824411 0.2853859 0.63927875
## aBreastFeed_total_stopped 0.1288869 1.7691646 0.07285186
## log2_age 2.3807006 0.7794891 3.05418070
## aSUP_Fe 0.1077969 2.2654153 0.04758371
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of serum Fe level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 1.8052234 | -0.8874043 | 4.497851 | 0.1888387 |
| VG vs OM | -0.7739504 | -3.8776292 | 2.329728 | 0.6250206 |
| VN vs VG | 2.5791737 | -0.3786597 | 5.537007 | 0.0874415 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),
remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),
exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.295
## Unadjusted ICC: 0.252
i = i+12.2.16 aVKFE
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aVKFE"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.3823 4.4650 13.971 < 2e-16 ***
## aBreastFeed_full_stopped 13.2154 4.4888 2.944 0.00419 **
## aBreastFeed_full_duration -0.5952 0.3710 -1.604 0.11239
## aBreastFeed_total_stopped 1.3796 2.3093 0.597 0.55185
## SEXM 0.7382 1.4869 0.496 0.62084
## GRPVG -0.6800 2.4266 -0.280 0.78000
## GRPVN -3.7722 2.0706 -1.822 0.07204 .
## aSUP_Fe 7.0698 3.2251 2.192 0.03114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 2.118 0.149349
## s(FAM) 38.95 87 0.897 0.000901 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.422 Deviance explained = 62.9%
## GCV = 75.421 Scale est. = 48.024 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 8.667 0.00419
## aBreastFeed_full_duration 1 2.574 0.11239
## aBreastFeed_total_stopped 1 0.357 0.55185
## SEX 1 0.247 0.62084
## GRP 2 1.955 0.14791
## aSUP_Fe 1 4.805 0.03114
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 2.118 0.149349
## s(FAM) 38.95 87.00 0.897 0.000901
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Se +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.983017 0.088714 67.441 <2e-16 ***
## aBreastFeed_full_stopped 0.273358 0.090998 3.004 0.0035 **
## aBreastFeed_full_duration -0.011441 0.007584 -1.509 0.1351
## aBreastFeed_total_stopped 0.005150 0.046104 0.112 0.9113
## SEXM 0.011662 0.030345 0.384 0.7017
## GRPVG -0.017728 0.049211 -0.360 0.7196
## GRPVN -0.073403 0.042327 -1.734 0.0865 .
## aSUP_Se 0.015734 0.120183 0.131 0.8962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1 1.055 0.307258
## s(FAM) 37.9 86 0.886 0.000824 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.409 Deviance explained = 61.6%
## GCV = 0.031298 Scale est. = 0.020177 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 9.024 0.0035
## aBreastFeed_full_duration 1 2.276 0.1351
## aBreastFeed_total_stopped 1 0.012 0.9113
## SEX 1 0.148 0.7017
## GRP 2 1.681 0.1922
## aSUP_Se 1 0.017 0.8962
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1.0 1.055 0.307258
## s(FAM) 37.9 86.0 0.886 0.000824
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue with the original scale.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Fe'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 64.4059077 4.0633714 15.8503621
## GRPVG -1.4747728 2.1520674 -0.6852819
## GRPVN -4.0158026 1.8766054 -2.1399291
## SEXM 1.2272099 1.5655842 0.7838671
## aBreastFeed_full_stopped 13.7748521 4.7908254 2.8752565
## aBreastFeed_full_duration -0.6906090 0.3880067 -1.7798896
## aBreastFeed_total_stopped -0.5705518 2.4145215 -0.2363002
## log2_age -0.4710794 1.0602540 -0.4443081
## aSUP_Fe 4.7728782 3.0771535 1.5510693
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of serum ferritin Fe binding capacity. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -4.015803 | -7.693882 | -0.3377235 | 0.0323605 |
| VG vs OM | -1.474773 | -5.692747 | 2.7432018 | 0.4931661 |
| VN vs VG | -2.541030 | -6.566632 | 1.4845727 | 0.2160265 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(
include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.386
## Unadjusted ICC: 0.342
i = i+12.2.17 aFERR
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aFERR"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.5495 9.9900 8.363 3.72e-13 ***
## aBreastFeed_full_stopped -62.3996 10.2822 -6.069 2.32e-08 ***
## aBreastFeed_full_duration 0.5551 0.5726 0.970 0.3346
## aBreastFeed_total_stopped -7.2663 3.6290 -2.002 0.0480 *
## SEXM 0.1113 2.3019 0.048 0.9615
## GRPVG -5.0793 3.3969 -1.495 0.1380
## GRPVN -3.4860 2.9029 -1.201 0.2326
## aSUP_Fe -10.8859 4.6701 -2.331 0.0218 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 7.764 8.511 3.186 0.0022 **
## s(FAM) 17.186 88.000 0.272 0.0562 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.382 Deviance explained = 53.1%
## GCV = 185.64 Scale est. = 139.65 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 36.829 2.32e-08
## aBreastFeed_full_duration 1 0.940 0.3346
## aBreastFeed_total_stopped 1 4.009 0.0480
## SEX 1 0.002 0.9615
## GRP 2 1.257 0.2890
## aSUP_Fe 1 5.433 0.0218
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 7.764 8.511 3.186 0.0022
## s(FAM) 17.186 88.000 0.272 0.0562
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Se +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.60045 0.41955 13.349 < 2e-16 ***
## aBreastFeed_full_stopped -1.63261 0.43461 -3.757 0.00029 ***
## aBreastFeed_full_duration 0.03298 0.03597 0.917 0.36133
## aBreastFeed_total_stopped -0.12395 0.21573 -0.575 0.56688
## SEXM -0.12517 0.14330 -0.873 0.38451
## GRPVG -0.24556 0.21730 -1.130 0.26116
## GRPVN -0.27089 0.18700 -1.449 0.15057
## aSUP_Se 0.53357 0.53390 0.999 0.32003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 12.157 0.000728 ***
## s(FAM) 24.25 87 0.421 0.025699 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.339 Deviance explained = 50.1%
## GCV = 0.70899 Scale est. = 0.53172 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Se + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 14.111 0.00029
## aBreastFeed_full_duration 1 0.841 0.36133
## aBreastFeed_total_stopped 1 0.330 0.56688
## SEX 1 0.763 0.38451
## GRP 2 1.163 0.31660
## aSUP_Se 1 0.999 0.32003
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 12.157 0.000728
## s(FAM) 24.25 87.00 0.421 0.025699
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. It seems that the aFERR change in multiplicative, rather than additive way. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Fe'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 5.13335065 0.36414370 14.0970465
## GRPVG -0.19398462 0.19276407 -1.0063318
## GRPVN -0.25351004 0.16723441 -1.5158964
## SEXM -0.15726658 0.13958403 -1.1266804
## aBreastFeed_full_stopped -1.59228598 0.42927251 -3.7092661
## aBreastFeed_full_duration 0.04131011 0.03474001 1.1891219
## aBreastFeed_total_stopped -0.17718814 0.21536031 -0.8227521
## log2_age 0.32987192 0.09488716 3.4764651
## aSUP_Fe -0.44268830 0.27576888 -1.6052874
res <- emm(mod_main)
suppl_table <- kbl(
res,
caption =
'Results of linear mixed-effects model of serum ferritin levels. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.2535100 | -0.5812835 | 0.0742634 | 0.1295455 |
| VG vs OM | -0.1939846 | -0.5717953 | 0.1838260 | 0.3142560 |
| VN vs VG | -0.0595254 | -0.4195823 | 0.3005315 | 0.7459186 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),
remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),
exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.272
## Unadjusted ICC: 0.214
i = i+12.2.18 aTRF
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aTRF"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.47916 0.17422 14.230 < 2e-16 ***
## aBreastFeed_full_stopped 0.53068 0.17519 3.029 0.00325 **
## aBreastFeed_full_duration -0.02465 0.01447 -1.704 0.09212 .
## aBreastFeed_total_stopped 0.04163 0.08971 0.464 0.64381
## SEXM 0.04111 0.05776 0.712 0.47851
## GRPVG -0.03762 0.09471 -0.397 0.69217
## GRPVN -0.15858 0.08038 -1.973 0.05177 .
## aSUP_Fe 0.28866 0.12591 2.293 0.02435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 1.820 0.18085
## s(FAM) 39.39 88 0.879 0.00114 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.425 Deviance explained = 63.1%
## GCV = 0.11495 Scale est. = 0.073131 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 9.176 0.00325
## aBreastFeed_full_duration 1 2.902 0.09212
## aBreastFeed_total_stopped 1 0.215 0.64381
## SEX 1 0.507 0.47851
## GRP 2 2.208 0.11618
## aSUP_Fe 1 5.256 0.02435
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 1.820 0.18085
## s(FAM) 39.39 88.00 0.879 0.00114
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
include = c('aSUP_Fe'),
remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.54999401 0.15553735 16.3947377
## GRPVG -0.07110078 0.08579585 -0.8287205
## GRPVN -0.16624507 0.07379917 -2.2526685
## SEXM 0.05247112 0.05951443 0.8816536
## aBreastFeed_full_stopped 0.54626083 0.18242962 2.9943647
## aBreastFeed_full_duration -0.02602305 0.01485284 -1.7520594
## aBreastFeed_total_stopped -0.01540385 0.09182296 -0.1677560
## log2_age -0.02651447 0.04057780 -0.6534231
## aSUP_Fe 0.20447129 0.12067443 1.6944045
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of serum transferin level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1662451 | -0.3108888 | -0.0216013 | 0.0242801 |
| VG vs OM | -0.0711008 | -0.2392576 | 0.0970560 | 0.4072626 |
| VN vs VG | -0.0951443 | -0.2535254 | 0.0632368 | 0.2390322 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(
include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.377
## Unadjusted ICC: 0.330
i = i+12.2.19 aSATTRF
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aSATTRF"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.97906 5.09171 4.317 3.9e-05 ***
## aBreastFeed_full_stopped -6.70051 5.12459 -1.308 0.194
## aBreastFeed_full_duration 0.44533 0.40280 1.106 0.272
## aBreastFeed_total_stopped 0.18633 2.50413 0.074 0.941
## SEXM -0.94681 1.61532 -0.586 0.559
## GRPVG -0.04314 2.49683 -0.017 0.986
## GRPVN 3.18337 2.12907 1.495 0.138
## aSUP_Fe -2.72106 3.40006 -0.800 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.403 1.654 6.243 0.00523 **
## s(FAM) 29.151 88.000 0.545 0.00980 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.348 Deviance explained = 53.4%
## GCV = 89.747 Scale est. = 63.731 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.710 0.194
## aBreastFeed_full_duration 1 1.222 0.272
## aBreastFeed_total_stopped 1 0.006 0.941
## SEX 1 0.344 0.559
## GRP 2 1.525 0.223
## aSUP_Fe 1 0.640 0.426
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.403 1.654 6.243 0.00523
## s(FAM) 29.151 88.000 0.545 0.00980
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Fe'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 16.35324830 4.2672915 3.83223139
## GRPVG -0.03228986 2.2589447 -0.01429422
## GRPVN 3.81566045 1.9597701 1.94699386
## SEXM -1.70386532 1.6357436 -1.04164572
## aBreastFeed_full_stopped -6.44160947 5.0305166 -1.28050656
## aBreastFeed_full_duration 0.43812856 0.4071078 1.07619790
## aBreastFeed_total_stopped 0.74619741 2.5237432 0.29567089
## log2_age 3.38235120 1.1119544 3.04180747
## aSUP_Fe -2.25049575 3.2316533 -0.69639147
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of transferin saturation. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 3.8156604 | -0.0254185 | 7.656739 | 0.0515355 |
| VG vs OM | -0.0322899 | -4.4597402 | 4.395160 | 0.9885952 |
| VN vs VG | 3.8479503 | -0.3714491 | 8.067350 | 0.0738694 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.267
## Unadjusted ICC: 0.224
i = i+12.2.20 aTRFINDEX
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aTRFINDEX"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.53819 0.35308 1.524 0.130645
## aBreastFeed_full_stopped 1.24975 0.35765 3.494 0.000713 ***
## aBreastFeed_full_duration -0.03609 0.02932 -1.231 0.221197
## aBreastFeed_total_stopped -0.11297 0.18110 -0.624 0.534212
## SEXM 0.12311 0.11718 1.051 0.295984
## GRPVG 0.04910 0.17859 0.275 0.783961
## GRPVN 0.01313 0.15245 0.086 0.931539
## aSUP_Fe 0.34523 0.24521 1.408 0.162294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1 13.24 0.000437 ***
## s(FAM) 25.3 88 0.41 0.046792 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.348 Deviance explained = 51.3%
## GCV = 0.4758 Scale est. = 0.35308 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 12.210 0.000713
## aBreastFeed_full_duration 1 1.516 0.221197
## aBreastFeed_total_stopped 1 0.389 0.534212
## SEX 1 1.104 0.295984
## GRP 2 0.040 0.961128
## aSUP_Fe 1 1.982 0.162294
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1.0 13.24 0.000437
## s(FAM) 25.3 88.0 0.41 0.046792
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.51104 0.25552 -2.000 0.0483 *
## aBreastFeed_full_stopped 1.09404 0.25811 4.239 5.1e-05 ***
## aBreastFeed_full_duration -0.03125 0.02080 -1.502 0.1362
## aBreastFeed_total_stopped 0.08315 0.12879 0.646 0.5201
## SEXM 0.08382 0.08325 1.007 0.3165
## GRPVG 0.08239 0.12716 0.648 0.5185
## GRPVN 0.03516 0.10853 0.324 0.7467
## aSUP_Fe 0.37311 0.17428 2.141 0.0348 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.16 1.278 19.373 1.14e-05 ***
## s(FAM) 26.15 88.000 0.457 0.0212 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.406 Deviance explained = 56%
## GCV = 0.23947 Scale est. = 0.17589 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 17.966 5.1e-05
## aBreastFeed_full_duration 1 2.257 0.1362
## aBreastFeed_total_stopped 1 0.417 0.5201
## SEX 1 1.014 0.3165
## GRP 2 0.210 0.8110
## aSUP_Fe 1 4.583 0.0348
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.160 1.278 19.373 1.14e-05
## s(FAM) 26.151 88.000 0.457 0.0212
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation slightly improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
include = c('aSUP_Fe'),
remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -0.16175577 0.19376737 -0.8347937
## GRPVG 0.07796802 0.10257321 0.7601207
## GRPVN 0.03487963 0.08898842 0.3919570
## SEXM 0.08290378 0.07427516 1.1161711
## aBreastFeed_full_stopped 1.00254138 0.22842357 4.3889576
## aBreastFeed_full_duration -0.03249353 0.01848578 -1.7577584
## aBreastFeed_total_stopped 0.21873192 0.11459707 1.9087043
## log2_age -0.27163505 0.05049116 -5.3798540
## aSUP_Fe 0.33845706 0.14674155 2.3064842
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of TRF index. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0348796 | -0.1395345 | 0.2092937 | 0.6950900 |
| VG vs OM | 0.0779680 | -0.1230718 | 0.2790078 | 0.4471824 |
| VN vs VG | -0.0430884 | -0.2346811 | 0.1485043 | 0.6593668 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.238
## Unadjusted ICC: 0.173
i = i+12.2.21 aSTRF
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aSTRF"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.19872 0.16067 7.461 4.14e-11 ***
## aBreastFeed_full_stopped 0.59769 0.16190 3.692 0.000373 ***
## aBreastFeed_full_duration -0.02013 0.01293 -1.557 0.122754
## aBreastFeed_total_stopped 0.01538 0.08020 0.192 0.848344
## SEXM 0.03200 0.05178 0.618 0.537975
## GRPVG -0.01915 0.08017 -0.239 0.811682
## GRPVN -0.05715 0.06834 -0.836 0.405146
## aSUP_Fe 0.25874 0.10916 2.370 0.019808 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.255 1.428 13.86 0.000106 ***
## s(FAM) 29.216 88.000 0.51 0.021984 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.399 Deviance explained = 57%
## GCV = 0.092394 Scale est. = 0.065668 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 13.629 0.000373
## aBreastFeed_full_duration 1 2.425 0.122754
## aBreastFeed_total_stopped 1 0.037 0.848344
## SEX 1 0.382 0.537975
## GRP 2 0.373 0.689671
## aSUP_Fe 1 5.618 0.019808
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.255 1.428 13.86 0.000106
## s(FAM) 29.216 88.000 0.51 0.021984
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Fe +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.287074 0.139400 2.059 0.042311 *
## aBreastFeed_full_stopped 0.507889 0.140234 3.622 0.000481 ***
## aBreastFeed_full_duration -0.016738 0.010353 -1.617 0.109398
## aBreastFeed_total_stopped 0.046116 0.064922 0.710 0.479318
## SEXM 0.020514 0.041702 0.492 0.623965
## GRPVG -0.005776 0.064991 -0.089 0.929379
## GRPVN -0.040734 0.055405 -0.735 0.464105
## aSUP_Fe 0.203685 0.087957 2.316 0.022816 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.036 2.494 9.394 9.8e-05 ***
## s(FAM) 31.760 88.000 0.598 0.0093 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.433 Deviance explained = 60.8%
## GCV = 0.059366 Scale est. = 0.04071 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Fe + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 13.117 0.000481
## aBreastFeed_full_duration 1 2.614 0.109398
## aBreastFeed_total_stopped 1 0.505 0.479318
## SEX 1 0.242 0.623965
## GRP 2 0.326 0.722962
## aSUP_Fe 1 5.363 0.022816
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.036 2.494 9.394 9.8e-05
## s(FAM) 31.760 88.000 0.598 0.0093
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation slightly improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Fe'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.46926832 0.10968743 4.2782324
## GRPVG 0.01172688 0.05806443 0.2019632
## GRPVN -0.02843617 0.05037438 -0.5644968
## SEXM 0.04688096 0.04204553 1.1150048
## aBreastFeed_full_stopped 0.46105950 0.12930554 3.5656593
## aBreastFeed_full_duration -0.01650168 0.01046439 -1.5769366
## aBreastFeed_total_stopped 0.07303085 0.06487087 1.1257881
## log2_age -0.13238459 0.02858193 -4.6317584
## aSUP_Fe 0.17206237 0.08306715 2.0713648
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(soluble transferin receptor level). CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0284362 | -0.1271681 | 0.0702958 | 0.5724161 |
| VG vs OM | 0.0117269 | -0.1020773 | 0.1255311 | 0.8399455 |
| VN vs VG | -0.0401630 | -0.1486194 | 0.0682934 | 0.4679587 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Fe'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Fe'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Fe'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Fe'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Fe'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.262
## Unadjusted ICC: 0.197
i = i+12.2.22 aHGB
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aHGB"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 122.74805 4.97703 24.663 <2e-16 ***
## aBreastFeed_full_stopped -1.91829 4.98365 -0.385 0.701
## aBreastFeed_full_duration 0.13677 0.35457 0.386 0.701
## aBreastFeed_total_stopped 0.01832 2.27774 0.008 0.994
## SEXM -1.56801 1.44375 -1.086 0.281
## GRPVG 1.80607 2.52695 0.715 0.477
## GRPVN 1.26256 2.11961 0.596 0.553
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.982 2.403 8.136 0.000579 ***
## s(FAM) 47.379 86.000 1.257 0.000201 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.573 Deviance explained = 75.9%
## GCV = 68.526 Scale est. = 38.353 n = 128
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.148 0.701
## aBreastFeed_full_duration 1 0.149 0.701
## aBreastFeed_total_stopped 1 0.000 0.994
## SEX 1 1.180 0.281
## GRP 2 0.289 0.750
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.982 2.403 8.136 0.000579
## s(FAM) 47.379 86.000 1.257 0.000201
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.932308 0.059156 117.188 <2e-16 ***
## aBreastFeed_full_stopped -0.019780 0.059305 -0.334 0.740
## aBreastFeed_full_duration 0.001477 0.004156 0.355 0.723
## aBreastFeed_total_stopped 0.003080 0.026730 0.115 0.909
## SEXM -0.019741 0.016954 -1.164 0.248
## GRPVG 0.019120 0.029492 0.648 0.519
## GRPVN 0.014925 0.024741 0.603 0.548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.104 2.555 8.018 0.000566 ***
## s(FAM) 46.729 86.000 1.226 0.000238 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.572 Deviance explained = 75.7%
## GCV = 0.0094269 Scale est. = 0.0053149 n = 128
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.111 0.740
## aBreastFeed_full_duration 1 0.126 0.723
## aBreastFeed_total_stopped 1 0.013 0.909
## SEX 1 1.356 0.248
## GRP 2 0.258 0.773
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.104 2.555 8.018 0.000566
## s(FAM) 46.729 86.000 1.226 0.000238
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
## main <- model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 121.9905347 3.8620803 31.5867422
## GRPVG 1.1369635 2.1171548 0.5370243
## GRPVN -0.4399365 1.8315289 -0.2402018
## SEXM -1.0657992 1.5306017 -0.6963270
## aBreastFeed_full_stopped -7.7221497 4.6434642 -1.6630148
## aBreastFeed_full_duration 0.2799735 0.3739679 0.7486564
## aBreastFeed_total_stopped -0.5898554 2.3110586 -0.2552317
## log2_age 4.3371619 1.0364402 4.1846716
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of hemoglobin level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.4399365 | -4.029667 | 3.149794 | 0.8101738 |
| VG vs OM | 1.1369635 | -3.012584 | 5.286511 | 0.5912508 |
| VN vs VG | -1.5769001 | -5.525154 | 2.371354 | 0.4337484 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.453
## Unadjusted ICC: 0.366
i = i+12.2.23 aMCV
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aMCV"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.8549 1.6263 49.716 < 2e-16 ***
## aBreastFeed_full_stopped -4.3842 1.6607 -2.640 0.00989 **
## aBreastFeed_full_duration 0.1343 0.1364 0.985 0.32750
## aBreastFeed_total_stopped 0.5073 0.8466 0.599 0.55062
## SEXM -0.6617 0.5486 -1.206 0.23114
## GRPVG 0.2353 0.9008 0.261 0.79461
## GRPVN 3.1766 0.7592 4.184 7.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 24.760 3.67e-06 ***
## s(FAM) 36.57 86 0.842 0.00114 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.551 Deviance explained = 70.5%
## GCV = 10.123 Scale est. = 6.5983 n = 128
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 6.970 0.00989
## aBreastFeed_full_duration 1 0.970 0.32750
## aBreastFeed_total_stopped 1 0.359 0.55062
## SEX 1 1.455 0.23114
## GRP 2 11.204 4.9e-05
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 24.760 3.67e-06
## s(FAM) 36.57 86.00 0.842 0.00114
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 78.5030691 1.4242874 55.1174339
## GRPVG -0.1263542 0.9334602 -0.1353611
## GRPVN 2.9747381 0.7853586 3.7877452
## SEXM -0.7844403 0.5540062 -1.4159413
## aBreastFeed_full_stopped -4.4061689 1.6763600 -2.6284145
## aBreastFeed_full_duration 0.1364613 0.1379682 0.9890783
## aBreastFeed_total_stopped 0.1671523 0.8593625 0.1945073
## log2_age 2.0174872 0.3859169 5.2277759
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of mean corpuscular volume. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 2.9747381 | 1.435464 | 4.514013 | 0.0001520 |
| VG vs OM | -0.1263542 | -1.955903 | 1.703194 | 0.8923264 |
| VN vs VG | 3.1010923 | 1.429286 | 4.772899 | 0.0002773 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.383
## Unadjusted ICC: 0.250
i = i+12.2.24 aPTH
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aPTH"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.740188 0.579897 3.001 0.00387 **
## aBreastFeed_full_stopped 0.912129 0.576207 1.583 0.11849
## aBreastFeed_full_duration 0.003497 0.048368 0.072 0.94259
## aBreastFeed_total_stopped 0.074439 0.306435 0.243 0.80887
## SEXM 0.192782 0.194414 0.992 0.32522
## GRPVG -0.099803 0.390103 -0.256 0.79892
## GRPVN 0.329237 0.326115 1.010 0.31660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 2.601 0.112
## s(FAM) 61.67 87 2.275 1.87e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.617 Deviance explained = 81.8%
## GCV = 1.2731 Scale est. = 0.60119 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 2.506 0.118
## aBreastFeed_full_duration 1 0.005 0.943
## aBreastFeed_total_stopped 1 0.059 0.809
## SEX 1 0.983 0.325
## GRP 2 0.935 0.398
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 2.601 0.112
## s(FAM) 61.67 87.00 2.275 1.87e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.067989450 0.49298273 4.19485174
## GRPVG -0.016995657 0.35350110 -0.04807809
## GRPVN 0.375608950 0.29563721 1.27050633
## SEXM 0.110783438 0.18881964 0.58671566
## aBreastFeed_full_stopped 0.747933025 0.56299165 1.32849754
## aBreastFeed_full_duration 0.007204965 0.04705299 0.15312449
## aBreastFeed_total_stopped -0.024917109 0.29353555 -0.08488617
## log2_age -0.136813968 0.12535498 -1.09141232
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of parathormone level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.3756089 | -0.2038293 | 0.9550472 | 0.2039043 |
| VG vs OM | -0.0169957 | -0.7098451 | 0.6758538 | 0.9616540 |
| VN vs VG | 0.3926046 | -0.2383901 | 1.0235993 | 0.2226588 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.600
## Unadjusted ICC: 0.557
i = i+12.2.25 aCros
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Ca),
!is.na(aSUP_D))
column_name
## [1] "aCros"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + aSUP_D +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.052102 0.227458 4.625 1.29e-05 ***
## aBreastFeed_full_stopped 0.188844 0.236859 0.797 0.427
## aBreastFeed_full_duration 0.013003 0.012868 1.010 0.315
## aBreastFeed_total_stopped -0.110060 0.080126 -1.374 0.173
## SEXM -0.006262 0.052808 -0.119 0.906
## GRPVG 0.036192 0.085240 0.425 0.672
## GRPVN 0.019901 0.070882 0.281 0.780
## aSUP_Ca -0.173916 0.142349 -1.222 0.225
## aSUP_D 0.028563 0.065643 0.435 0.665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 7.75 8.448 2.517 0.0142 *
## s(FAM) 27.01 86.000 0.500 0.0168 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.36 Deviance explained = 57.1%
## GCV = 0.093688 Scale est. = 0.06239 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + aSUP_D +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.636 0.427
## aBreastFeed_full_duration 1 1.021 0.315
## aBreastFeed_total_stopped 1 1.887 0.173
## SEX 1 0.014 0.906
## GRP 2 0.092 0.912
## aSUP_Ca 1 1.493 0.225
## aSUP_D 1 0.189 0.665
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 7.750 8.448 2.517 0.0142
## s(FAM) 27.013 86.000 0.500 0.0168
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + aSUP_D +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.005831 0.256938 -0.023 0.982
## aBreastFeed_full_stopped 0.242759 0.267456 0.908 0.367
## aBreastFeed_full_duration 0.012185 0.014464 0.842 0.402
## aBreastFeed_total_stopped -0.108066 0.089873 -1.202 0.232
## SEXM 0.007417 0.059199 0.125 0.901
## GRPVG 0.080354 0.094759 0.848 0.399
## GRPVN 0.075429 0.078903 0.956 0.342
## aSUP_Ca -0.212942 0.159533 -1.335 0.185
## aSUP_D 0.028037 0.073220 0.383 0.703
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 8.14 8.688 3.096 0.00385 **
## s(FAM) 24.66 86.000 0.442 0.02392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.365 Deviance explained = 56.4%
## GCV = 0.11837 Scale est. = 0.080598 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + aSUP_D +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.824 0.367
## aBreastFeed_full_duration 1 0.710 0.402
## aBreastFeed_total_stopped 1 1.446 0.232
## SEX 1 0.016 0.901
## GRP 2 0.538 0.586
## aSUP_Ca 1 1.782 0.185
## aSUP_D 1 0.147 0.703
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 8.140 8.688 3.096 0.00385
## s(FAM) 24.663 86.000 0.442 0.02392
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Ca', 'aSUP_D'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.015552802 0.15963859 0.09742508
## GRPVG 0.084899520 0.08989729 0.94440574
## GRPVN 0.055022493 0.07511070 0.73255200
## SEXM 0.053202344 0.06082597 0.87466496
## aBreastFeed_full_stopped 0.167486445 0.18624144 0.89929743
## aBreastFeed_full_duration 0.025272107 0.01503215 1.68120409
## aBreastFeed_total_stopped -0.045919713 0.09081623 -0.50563334
## log2_age -0.039324771 0.04174356 -0.94205596
## aSUP_Ca -0.199618968 0.16431147 -1.21488148
## aSUP_D -0.006484379 0.07229079 -0.08969855
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(beta cross laps). CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0550225 | -0.0921918 | 0.2022368 | 0.4638317 |
| VG vs OM | 0.0848995 | -0.0912959 | 0.2610950 | 0.3449623 |
| VN vs VG | -0.0298770 | -0.1905284 | 0.1307743 | 0.7154825 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Ca', 'aSUP_D'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Ca', 'aSUP_D'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Ca', 'aSUP_D'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Ca', 'aSUP_D'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Ca', 'aSUP_D'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Ca', 'aSUP_D'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.137
## Unadjusted ICC: 0.126
i = i+12.2.26 aP1NP
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Ca),
!is.na(aSUP_D))
column_name
## [1] "aP1NP"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + aSUP_D +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 609.331 102.691 5.934 3.29e-08 ***
## aBreastFeed_full_stopped 119.996 109.182 1.099 0.2741
## aBreastFeed_full_duration -11.209 6.585 -1.702 0.0914 .
## aBreastFeed_total_stopped -1.849 40.760 -0.045 0.9639
## SEXM 34.222 26.788 1.277 0.2040
## GRPVG 67.598 39.787 1.699 0.0921 .
## GRPVN 60.960 33.120 1.841 0.0683 .
## aSUP_Ca 51.881 71.521 0.725 0.4697
## aSUP_D 3.413 31.727 0.108 0.9145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 4.279 5.31 29.744 <2e-16 ***
## s(FAM) 4.328 86.00 0.053 0.377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.757 Deviance explained = 78.8%
## GCV = 24348 Scale est. = 21075 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + aSUP_D +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.208 0.2741
## aBreastFeed_full_duration 1 2.898 0.0914
## aBreastFeed_total_stopped 1 0.002 0.9639
## SEX 1 1.632 0.2040
## GRP 2 2.073 0.1305
## aSUP_Ca 1 0.526 0.4697
## aSUP_D 1 0.012 0.9145
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 4.279 5.310 29.744 <2e-16
## s(FAM) 4.328 86.000 0.053 0.377
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_Ca', 'aSUP_D'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 972.884093 84.051812 11.5748140
## GRPVG 63.171578 47.332104 1.3346455
## GRPVN 41.883265 39.546769 1.0590819
## SEXM 62.890873 32.025671 1.9637645
## aBreastFeed_full_stopped 106.687484 98.058559 1.0879977
## aBreastFeed_full_duration -15.038083 7.914623 -1.9000379
## aBreastFeed_total_stopped -58.784503 47.815935 -1.2293915
## log2_age -203.808015 21.978533 -9.2730492
## aSUP_Ca 77.181600 86.512147 0.8921476
## aSUP_D 4.410471 38.062050 0.1158758
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of procollagen type I level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 41.88327 | -35.62698 | 119.39351 | 0.2895625 |
| VG vs OM | 63.17158 | -29.59764 | 155.94080 | 0.1819924 |
| VN vs VG | -21.28831 | -105.87337 | 63.29674 | 0.6218130 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Ca', 'aSUP_D'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Ca', 'aSUP_D'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Ca', 'aSUP_D'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Ca', 'aSUP_D'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Ca', 'aSUP_D'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Ca', 'aSUP_D'),
exclude = c('log2_age'))
## boundary (singular) fit: see help('isSingular')Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.231
## Unadjusted ICC: 0.067
i = i+12.2.27 aUI
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aUI"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 257.222 76.274 3.372 0.00109 **
## aBreastFeed_full_stopped -63.013 75.396 -0.836 0.40547
## aBreastFeed_full_duration -4.366 7.030 -0.621 0.53604
## aBreastFeed_total_stopped 4.090 44.541 0.092 0.92704
## SEXM 25.113 27.523 0.912 0.36393
## GRPVG -13.366 40.188 -0.333 0.74020
## GRPVN -22.580 35.961 -0.628 0.53162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 0.373 0.543
## s(FAM) 12.47 76 0.204 0.165
##
## R-sq.(adj) = 0.108 Deviance explained = 26.5%
## GCV = 22266 Scale est. = 18196 n = 112
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.698 0.405
## aBreastFeed_full_duration 1 0.386 0.536
## aBreastFeed_total_stopped 1 0.008 0.927
## SEX 1 0.833 0.364
## GRP 2 0.197 0.821
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 0.373 0.543
## s(FAM) 12.47 76.00 0.204 0.165
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.04257 0.63343 12.697 < 2e-16 ***
## aBreastFeed_full_stopped -0.75174 0.60297 -1.247 0.21674
## aBreastFeed_full_duration 0.02367 0.05582 0.424 0.67288
## aBreastFeed_total_stopped -0.14546 0.36373 -0.400 0.69047
## SEXM 0.32294 0.22369 1.444 0.15338
## GRPVG -0.39657 0.36937 -1.074 0.28675
## GRPVN -0.98035 0.32468 -3.019 0.00355 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 0.028 0.86820
## s(FAM) 35.42 76 0.857 0.00589 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.428 Deviance explained = 64.7%
## GCV = 1.4473 Scale est. = 0.88624 n = 112
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.554 0.2167
## aBreastFeed_full_duration 1 0.180 0.6729
## aBreastFeed_total_stopped 1 0.160 0.6905
## SEX 1 2.084 0.1534
## GRP 2 4.881 0.0104
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 0.028 0.86820
## s(FAM) 35.42 76.00 0.857 0.00589
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation better
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 7.9429665929 0.40575072 19.57597642
## GRPVG -0.3741851415 0.25172558 -1.48648040
## GRPVN -0.7851984224 0.22803581 -3.44331196
## SEXM 0.2823316997 0.18153910 1.55521156
## aBreastFeed_full_stopped -0.6161899099 0.50279201 -1.22553639
## aBreastFeed_full_duration 0.0006774979 0.04665396 0.01452177
## aBreastFeed_total_stopped -0.1679532753 0.29429818 -0.57069084
## log2_age 0.0868093119 0.13085245 0.66341374
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of urinary iodine level . CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.7851984 | -1.2321404 | -0.3382565 | 0.0005746 |
| VG vs OM | -0.3741851 | -0.8675582 | 0.1191879 | 0.1371521 |
| VN vs VG | -0.4110133 | -0.8597072 | 0.0376807 | 0.0725949 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.304
## Unadjusted ICC: 0.261
i = i+12.2.28 aUREA
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name),
log2_age_2 = log2_age**2) %>%
filter(!is.na(outcome))
column_name
## [1] "aUREA"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.45083 0.58839 5.865 8.1e-08 ***
## aBreastFeed_full_stopped 1.02339 0.60190 1.700 0.09268 .
## aBreastFeed_full_duration -0.06073 0.04243 -1.431 0.15592
## aBreastFeed_total_stopped 0.43588 0.26308 1.657 0.10118
## SEXM 0.28655 0.17168 1.669 0.09874 .
## GRPVG -0.18911 0.27425 -0.690 0.49233
## GRPVN -0.61662 0.23324 -2.644 0.00974 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.612 3.183 4.241 0.00616 **
## s(FAM) 37.197 88.000 0.778 0.00258 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.579 Deviance explained = 72.5%
## GCV = 0.99419 Scale est. = 0.64429 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 2.891 0.0927
## aBreastFeed_full_duration 1 2.049 0.1559
## aBreastFeed_total_stopped 1 2.745 0.1012
## SEX 1 2.786 0.0987
## GRP 2 3.799 0.0262
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.612 3.183 4.241 0.00616
## s(FAM) 37.197 88.000 0.778 0.00258
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, include = c('log2_age_2'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.44892379 0.51258305 6.7285171
## GRPVG -0.17017796 0.24549610 -0.6932002
## GRPVN -0.54650504 0.21336673 -2.5613414
## SEXM 0.23096388 0.17993118 1.2836234
## aBreastFeed_full_stopped 0.31493160 0.60853357 0.5175254
## aBreastFeed_full_duration -0.07411729 0.04429197 -1.6733800
## aBreastFeed_total_stopped 0.45065791 0.27139231 1.6605405
## log2_age 1.12052504 0.27356103 4.0960697
## log2_age_2 -0.28005981 0.08305899 -3.3718183
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of urea level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.5465050 | -0.9646962 | -0.1283139 | 0.0104269 |
| VG vs OM | -0.1701780 | -0.6513415 | 0.3109856 | 0.4881839 |
| VN vs VG | -0.3763271 | -0.8346064 | 0.0819522 | 0.1075133 |
Leave-one-factor models
Open code
### main model but non-robust
mod_main <- rlme(include = c('log2_age_2'),remove_random = TRUE)
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('log2_age_2'),exclude = c('SEX'),
remove_random = TRUE)
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('log2_age_2'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'),
remove_random = TRUE)
### model without diet groups
mod_ndiet <- rlme(include = c('log2_age_2'),
exclude = c('GRP'),
remove_random = TRUE)
### model without log2_age_2
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'),
include = c('log2_age_2'),remove_random = TRUE)Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## Warning: `model` has no random effects.
## NULL
i = i+12.2.29 aCREA
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aCREA"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(aAGE) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.0851 2.0091 12.984 <2e-16 ***
## aBreastFeed_full_stopped 2.1944 2.2108 0.993 0.323
## aBreastFeed_full_duration -0.2018 0.1852 -1.090 0.278
## aBreastFeed_total_stopped -0.6274 1.0611 -0.591 0.556
## SEXM 0.3388 0.7476 0.453 0.651
## GRPVG -0.5692 1.1110 -0.512 0.610
## GRPVN -1.3896 0.9561 -1.453 0.149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.518 1.848 98.142 <2e-16 ***
## s(FAM) 21.927 88.000 0.393 0.0147 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.738 Deviance explained = 79.7%
## GCV = 19.084 Scale est. = 14.716 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.985 0.323
## aBreastFeed_full_duration 1 1.187 0.278
## aBreastFeed_total_stopped 1 0.350 0.556
## SEX 1 0.205 0.651
## GRP 2 1.089 0.341
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.518 1.848 98.142 <2e-16
## s(FAM) 21.927 88.000 0.393 0.0147
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(aAGE) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.623918 0.114019 40.554 <2e-16 ***
## aBreastFeed_full_stopped 0.134109 0.123128 1.089 0.279
## aBreastFeed_full_duration -0.009475 0.010274 -0.922 0.359
## aBreastFeed_total_stopped -0.029880 0.060658 -0.493 0.623
## SEXM 0.018897 0.041596 0.454 0.651
## GRPVG -0.028555 0.061805 -0.462 0.645
## GRPVN -0.078300 0.053069 -1.475 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.063 2.552 57.442 <2e-16 ***
## s(FAM) 22.621 88.000 0.386 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.722 Deviance explained = 78.7%
## GCV = 0.058624 Scale est. = 0.044658 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.186 0.279
## aBreastFeed_full_duration 1 0.851 0.359
## aBreastFeed_total_stopped 1 0.243 0.623
## SEX 1 0.206 0.651
## GRP 2 1.142 0.323
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.063 2.552 57.442 <2e-16
## s(FAM) 22.621 88.000 0.386 0.0262
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 4.46250973 0.11528960 38.7069584
## GRPVG -0.05970886 0.06193321 -0.9640847
## GRPVN -0.10935941 0.05369952 -2.0365062
## SEXM 0.03302511 0.04484794 0.7363795
## aBreastFeed_full_stopped -0.11770817 0.13704639 -0.8588929
## aBreastFeed_full_duration -0.00847680 0.01116296 -0.7593686
## aBreastFeed_total_stopped -0.07609532 0.06716510 -1.1329593
## log2_age 0.31510609 0.02944252 10.7024174
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(creatinine) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1093594 | -0.2146085 | -0.0041103 | 0.0416995 |
| VG vs OM | -0.0597089 | -0.1810957 | 0.0616780 | 0.3350035 |
| VN vs VG | -0.0496505 | -0.1653684 | 0.0660673 | 0.4003741 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.169
## Unadjusted ICC: 0.062
i = i+12.2.30 aUA
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aUA"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 192.3535 23.0389 8.349 1.51e-12 ***
## aBreastFeed_full_stopped 30.9160 23.5010 1.316 0.192
## aBreastFeed_full_duration 0.8395 1.9583 0.429 0.669
## aBreastFeed_total_stopped 5.5272 11.9410 0.463 0.645
## SEXM 3.2065 7.8328 0.409 0.683
## GRPVG -5.1456 13.1704 -0.391 0.697
## GRPVN -7.8236 11.1575 -0.701 0.485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1 0.154 0.695498
## s(FAM) 43.8 88 1.093 0.000231 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.452 Deviance explained = 66.3%
## GCV = 2095.7 Scale est. = 1279.5 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.731 0.192
## aBreastFeed_full_duration 1 0.184 0.669
## aBreastFeed_total_stopped 1 0.214 0.645
## SEX 1 0.168 0.683
## GRP 2 0.246 0.782
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.0 1.0 0.154 0.695498
## s(FAM) 43.8 88.0 1.093 0.000231
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 191.5011255 18.983604 10.0877117
## GRPVG -4.9438565 13.230094 -0.3736826
## GRPVN -2.3808811 11.184973 -0.2128643
## SEXM 3.9727707 7.279530 0.5457455
## aBreastFeed_full_stopped 34.5743917 21.691870 1.5938871
## aBreastFeed_full_duration -0.9849698 1.817146 -0.5420421
## aBreastFeed_total_stopped 2.9853057 11.255966 0.2652199
## log2_age 4.7060688 4.838499 0.9726299
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of uric acid level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -2.380881 | -24.30302 | 19.54126 | 0.8314328 |
| VG vs OM | -4.943856 | -30.87436 | 20.98665 | 0.7086404 |
| VN vs VG | 2.562975 | -20.97969 | 26.10564 | 0.8310370 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.435
## Unadjusted ICC: 0.411
i = i+12.2.31 aVIT_AKTB12
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name),
log2_age_2 = log2_age**2) %>%
filter(!is.na(outcome),
!is.na(aSup_B12))
column_name
## [1] "aVIT_AKTB12"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSup_B12 +
s(log2_age_2) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age_2) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.089 30.648 1.569 0.1216
## aBreastFeed_full_stopped 79.211 33.534 2.362 0.0212 *
## aBreastFeed_full_duration -4.839 2.764 -1.751 0.0847 .
## aBreastFeed_total_stopped 6.203 17.401 0.356 0.7227
## SEXM -8.574 11.489 -0.746 0.4582
## GRPVG -17.486 25.055 -0.698 0.4878
## GRPVN 34.524 24.722 1.396 0.1674
## aSup_B12 48.391 21.189 2.284 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age_2) 2.69 3.223 2.096 0.105
## s(FAM) 56.27 86.000 2.160 9.07e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.698 Deviance explained = 85.1%
## GCV = 4239.6 Scale est. = 2072.5 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age_2) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 5.580 0.0212
## aBreastFeed_full_duration 1 3.066 0.0847
## aBreastFeed_total_stopped 1 0.127 0.7227
## SEX 1 0.557 0.4582
## GRP 2 3.611 0.0327
## aSup_B12 1 5.215 0.0257
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age_2) 2.690 3.223 2.096 0.105
## s(FAM) 56.273 86.000 2.160 9.07e-07
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSup_B12 +
s(log2_age_2) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age_2) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.43921 0.28279 19.234 < 2e-16 ***
## aBreastFeed_full_stopped 1.29622 0.31183 4.157 9.33e-05 ***
## aBreastFeed_full_duration -0.04785 0.02570 -1.862 0.0670 .
## aBreastFeed_total_stopped 0.19678 0.15976 1.232 0.2224
## SEXM -0.11325 0.10658 -1.063 0.2918
## GRPVG -0.24065 0.22615 -1.064 0.2911
## GRPVN 0.26439 0.22321 1.185 0.2404
## aSup_B12 0.49931 0.19189 2.602 0.0114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age_2) 2.506 3.016 2.767 0.0483 *
## s(FAM) 53.226 86.000 1.902 2.1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.716 Deviance explained = 85.3%
## GCV = 0.36607 Scale est. = 0.18798 n = 131
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age_2) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 17.279 9.33e-05
## aBreastFeed_full_duration 1 3.466 0.0670
## aBreastFeed_total_stopped 1 1.517 0.2224
## SEX 1 1.129 0.2918
## GRP 2 4.088 0.0211
## aSup_B12 1 6.771 0.0114
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age_2) 2.506 3.016 2.767 0.0483
## s(FAM) 53.226 86.000 1.902 2.1e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, include = c('log2_age_2', 'aSup_B12'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 5.81633638 0.30177105 19.2740040
## GRPVG -0.31206834 0.21407573 -1.4577474
## GRPVN 0.29838441 0.20920353 1.4262877
## SEXM -0.06554295 0.09910917 -0.6613207
## aBreastFeed_full_stopped 0.61497174 0.34869992 1.7636131
## aBreastFeed_full_duration -0.04817121 0.02368617 -2.0337273
## aBreastFeed_total_stopped 0.13626617 0.15186935 0.8972592
## log2_age 0.49929784 0.15266233 3.2706028
## log2_age_2 -0.12352357 0.04658375 -2.6516451
## aSup_B12 0.49176271 0.17968360 2.7368258
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(active vitamin B12) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.2983844 | -0.1116470 | 0.7084158 | 0.1537853 |
| VG vs OM | -0.3120683 | -0.7316490 | 0.1075124 | 0.1449102 |
| VN vs VG | 0.6104527 | 0.2832585 | 0.9376470 | 0.0002554 |
Leave-one-factor models
Open code
### main model but non-robust
mod_main <- rlme(include = c('log2_age_2', 'aSup_B12'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('log2_age_2', 'aSup_B12'),
remove_random = TRUE)
### model without sex
mod_nsex <- rlme( include = c('log2_age_2', 'aSup_B12'),
exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(
include = c('log2_age_2', 'aSup_B12'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(
include = c('log2_age_2', 'aSup_B12'),
exclude = c('GRP'))
### model without other cov - log2_age_2 and supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(
exclude = c('log2_age'),
include = c('log2_age_2', 'aSup_B12'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.578
## Unadjusted ICC: 0.346
i = i+12.2.32 aHCY
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSup_B12))
column_name
## [1] "aHCY"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSup_B12 +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.15455 1.51935 9.316 1.36e-14 ***
## aBreastFeed_full_stopped -4.86855 1.55739 -3.126 0.00243 **
## aBreastFeed_full_duration -0.05625 0.10825 -0.520 0.60470
## aBreastFeed_total_stopped -0.42777 0.68795 -0.622 0.53575
## SEXM 0.94094 0.46216 2.036 0.04490 *
## GRPVG 0.38210 0.90201 0.424 0.67293
## GRPVN -0.31762 0.86082 -0.369 0.71307
## aSup_B12 -1.28970 0.77513 -1.664 0.09987 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.111 2.602 1.393 0.1728
## s(FAM) 24.802 78.000 0.517 0.0155 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.521 Deviance explained = 65.8%
## GCV = 6.4673 Scale est. = 4.5699 n = 119
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 9.772 0.00243
## aBreastFeed_full_duration 1 0.270 0.60470
## aBreastFeed_total_stopped 1 0.387 0.53575
## SEX 1 4.145 0.04490
## GRP 2 0.507 0.60435
## aSup_B12 1 2.768 0.09987
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.111 2.602 1.393 0.1728
## s(FAM) 24.802 78.000 0.517 0.0155
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSup_B12 +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.75616 0.21431 17.526 <2e-16 ***
## aBreastFeed_full_stopped -0.51366 0.21638 -2.374 0.0206 *
## aBreastFeed_full_duration -0.01620 0.01507 -1.074 0.2866
## aBreastFeed_total_stopped -0.07472 0.09806 -0.762 0.4488
## SEXM 0.15144 0.06501 2.330 0.0230 *
## GRPVG 0.04349 0.14122 0.308 0.7591
## GRPVN -0.14554 0.13490 -1.079 0.2847
## aSup_B12 -0.20281 0.11970 -1.694 0.0950 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.027 2.458 1.548 0.138
## s(FAM) 44.700 78.000 1.449 9.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.66 Deviance explained = 81.5%
## GCV = 0.12525 Scale est. = 0.067648 n = 119
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 5.636 0.0206
## aBreastFeed_full_duration 1 1.155 0.2866
## aBreastFeed_total_stopped 1 0.581 0.4488
## SEX 1 5.427 0.0230
## GRP 2 1.612 0.2074
## aSup_B12 1 2.871 0.0950
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.027 2.458 1.548 0.138
## s(FAM) 44.700 78.000 1.449 9.16e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
include = c('aSup_B12'),
remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.764273420 0.17399320 21.6346003
## GRPVG 0.015233446 0.12441901 0.1224366
## GRPVN -0.144774193 0.11861983 -1.2204890
## SEXM 0.213406760 0.06763601 3.1552242
## aBreastFeed_full_stopped -0.699411784 0.20640200 -3.3885901
## aBreastFeed_full_duration 0.003676956 0.01609629 0.2284350
## aBreastFeed_total_stopped -0.081000594 0.10000649 -0.8099534
## log2_age 0.020740662 0.04524239 0.4584343
## aSup_B12 -0.183737288 0.10861244 -1.6916782
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(homocysteine) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1447742 | -0.3772648 | 0.0877164 | 0.2222796 |
| VG vs OM | 0.0152334 | -0.2286233 | 0.2590902 | 0.9025532 |
| VN vs VG | -0.1600076 | -0.3459109 | 0.0258957 | 0.0916134 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c("aSup_B12"))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
include = c("aSup_B12"),
remove_random = TRUE
)
### model without sex
mod_nsex <- rlme(
include = c("aSup_B12"),
exclude = c("SEX")
)
### model without breastfeeding-related predictors
mod_nbf <- rlme(
include = c("aSup_B12"),
exclude = c(
"aBreastFeed_full_stopped",
"aBreastFeed_total_stopped",
"aBreastFeed_full_duration"
)
)
### model without diet groups
mod_ndiet <- rlme(include = c("aSup_B12"), exclude = c("GRP"))
### model without other cov.
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(
include = c("aSup_B12"),
exclude = c("log2_age")
)Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.528
## Unadjusted ICC: 0.335
i = i+12.2.33 aMMA
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSup_B12))
column_name
## [1] "aMMA"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSup_B12 +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1202.485 166.634 7.216 7.73e-11 ***
## aBreastFeed_full_stopped -841.224 168.529 -4.992 2.31e-06 ***
## aBreastFeed_full_duration -4.972 12.373 -0.402 0.689
## aBreastFeed_total_stopped -18.384 79.248 -0.232 0.817
## SEXM 7.104 51.644 0.138 0.891
## GRPVG 30.632 90.395 0.339 0.735
## GRPVN -89.296 88.826 -1.005 0.317
## aSup_B12 -79.658 77.861 -1.023 0.309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.000 1 1.730 0.191
## s(FAM) 5.775 82 0.076 0.347
##
## R-sq.(adj) = 0.401 Deviance explained = 46.8%
## GCV = 85718 Scale est. = 75422 n = 123
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 24.916 2.31e-06
## aBreastFeed_full_duration 1 0.162 0.689
## aBreastFeed_total_stopped 1 0.054 0.817
## SEX 1 0.019 0.891
## GRP 2 1.613 0.204
## aSup_B12 1 1.047 0.309
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.000 1.000 1.730 0.191
## s(FAM) 5.775 82.000 0.076 0.347
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSup_B12 +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.77351 0.43056 22.700 < 2e-16 ***
## aBreastFeed_full_stopped -1.47317 0.42272 -3.485 0.000847 ***
## aBreastFeed_full_duration -0.02739 0.02982 -0.919 0.361309
## aBreastFeed_total_stopped -0.14448 0.19600 -0.737 0.463470
## SEXM 0.10635 0.12521 0.849 0.398537
## GRPVG -0.01633 0.25586 -0.064 0.949293
## GRPVN -0.50984 0.25349 -2.011 0.048095 *
## aSup_B12 -0.33286 0.21754 -1.530 0.130424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.349 1.557 1.019 0.270367
## s(FAM) 42.597 82.000 1.138 0.000462 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.66 Deviance explained = 80.2%
## GCV = 0.48894 Scale est. = 0.28245 n = 123
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSup_B12 + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 12.145 0.000847
## aBreastFeed_full_duration 1 0.844 0.361309
## aBreastFeed_total_stopped 1 0.543 0.463470
## SEX 1 0.721 0.398537
## GRP 2 3.892 0.024889
## aSup_B12 1 2.341 0.130424
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.349 1.557 1.019 0.270367
## s(FAM) 42.597 82.000 1.138 0.000462
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. Even though it’s not perfect we will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(
main = TRUE,
include = c('aSup_B12'),
remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 9.80393388 0.28533468 34.3594188
## GRPVG -0.06652829 0.21863678 -0.3042868
## GRPVN -0.57940865 0.21730384 -2.6663525
## SEXM 0.15066327 0.09579644 1.5727440
## aBreastFeed_full_stopped -1.45766120 0.31105850 -4.6861320
## aBreastFeed_full_duration -0.02594862 0.02282983 -1.1366101
## aBreastFeed_total_stopped -0.15571146 0.15163880 -1.0268576
## log2_age -0.08152703 0.06647689 -1.2263966
## aSup_B12 -0.25480297 0.18494890 -1.3776939
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(methylmalonic acid) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.5794087 | -1.0053164 | -0.1535009 | 0.0076679 |
| VG vs OM | -0.0665283 | -0.4950485 | 0.3619919 | 0.7609094 |
| VN vs VG | -0.5128804 | -0.8398318 | -0.1859289 | 0.0021082 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c("aSup_B12"))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
include = c("aSup_B12"),
remove_random = TRUE
)
### model without sex
mod_nsex <- rlme(
include = c("aSup_B12"),
exclude = c("SEX")
)
### model without breastfeeding-related predictors
mod_nbf <- rlme(
include = c("aSup_B12"),
exclude = c(
"aBreastFeed_full_stopped",
"aBreastFeed_total_stopped",
"aBreastFeed_full_duration"
)
)
### model without diet groups
mod_ndiet <- rlme(include = c("aSup_B12"), exclude = c("GRP"))
### model without other cov.
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(
include = c("aSup_B12"),
exclude = c("log2_age")
)Putting key results together
Open code
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.437
## Unadjusted ICC: 0.250
i = i+12.2.34 aVIT_D
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i + ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(
!is.na(outcome),
!is.na(aSUP_D)
)
column_name
## [1] "aVIT_D"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_D +
s(log2_age) +
s(FAM, bs = "re"),
data = dat_mod
)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_D + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.6826 14.7562 4.858 7.97e-06 ***
## aBreastFeed_full_stopped 16.7005 14.5626 1.147 0.2557
## aBreastFeed_full_duration -1.3528 1.0589 -1.278 0.2060
## aBreastFeed_total_stopped -0.7997 6.7279 -0.119 0.9058
## SEXM 1.2436 4.3318 0.287 0.7750
## GRPVG 10.6565 8.4490 1.261 0.2118
## GRPVN 12.6014 7.0857 1.778 0.0801 .
## aSUP_D 7.3784 6.0060 1.229 0.2237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.23 2.697 1.500 0.174
## s(FAM) 58.59 88.000 2.429 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.682 Deviance explained = 84.5%
## GCV = 610.63 Scale est. = 294.66 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_D + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.315 0.256
## aBreastFeed_full_duration 1 1.632 0.206
## aBreastFeed_total_stopped 1 0.014 0.906
## SEX 1 0.082 0.775
## GRP 2 1.628 0.204
## aSUP_D 1 1.509 0.224
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.230 2.697 1.500 0.174
## s(FAM) 58.591 88.000 2.429 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_D +
s(log2_age) +
s(FAM, bs = "re"),
data = dat_mod
)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_D + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.116517 0.215800 28.343 <2e-16 ***
## aBreastFeed_full_stopped 0.213326 0.213165 1.001 0.3206
## aBreastFeed_full_duration -0.020286 0.015687 -1.293 0.2005
## aBreastFeed_total_stopped 0.000527 0.099134 0.005 0.9958
## SEXM 0.015259 0.064025 0.238 0.8124
## GRPVG 0.214343 0.123161 1.740 0.0865 .
## GRPVN 0.248801 0.103243 2.410 0.0188 *
## aSUP_D 0.138762 0.088189 1.573 0.1204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.12 2.564 1.721 0.134
## s(FAM) 57.07 88.000 2.294 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.692 Deviance explained = 84.6%
## GCV = 0.13393 Scale est. = 0.066274 n = 133
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_D + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 1.002 0.3206
## aBreastFeed_full_duration 1 1.672 0.2005
## aBreastFeed_total_stopped 1 0.000 0.9958
## SEX 1 0.057 0.8124
## GRP 2 3.007 0.0563
## aSUP_D 1 2.476 0.1204
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.120 2.564 1.721 0.134
## s(FAM) 57.068 88.000 2.294 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_D'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.148132007 0.16469708 37.3299384
## GRPVG 0.264362046 0.12385801 2.1343961
## GRPVN 0.282685631 0.10385865 2.7218303
## SEXM 0.008431574 0.06062285 0.1390824
## aBreastFeed_full_stopped 0.235689045 0.17659025 1.3346662
## aBreastFeed_full_duration -0.014773044 0.01500834 -0.9843220
## aBreastFeed_total_stopped 0.048562023 0.09383121 0.5175466
## log2_age -0.109857130 0.03968015 -2.7685667
## aSUP_D 0.137831420 0.08628554 1.5973873
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(vitamin D serum) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.2826856 | 0.0791264 | 0.4862449 | 0.0064921 |
| VG vs OM | 0.2643620 | 0.0216048 | 0.5071193 | 0.0328104 |
| VN vs VG | 0.0183236 | -0.1920903 | 0.2287375 | 0.8644749 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_D'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_D'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_D'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_D'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_D'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_D'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.630
## Unadjusted ICC: 0.526
i = i+12.2.35 aFOLAT
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i + ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(
!is.na(outcome),
!is.na(aSUP_FOL)
)
column_name
## [1] "aFOLAT"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_FOL +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_FOL + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.62157 1.95942 5.931 8.14e-08 ***
## aBreastFeed_full_stopped 1.28782 1.98910 0.647 0.519282
## aBreastFeed_full_duration 0.01750 0.16778 0.104 0.917192
## aBreastFeed_total_stopped 0.86410 1.01781 0.849 0.398537
## SEXM 0.08888 0.66824 0.133 0.894540
## GRPVG 4.49460 1.14098 3.939 0.000179 ***
## GRPVN 3.93983 0.96862 4.067 0.000114 ***
## aSUP_FOL 1.81436 2.23353 0.812 0.419118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 13.997 0.000351 ***
## s(FAM) 46.31 87 1.232 0.000126 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.598 Deviance explained = 76.5%
## GCV = 15.025 Scale est. = 8.7289 n = 132
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_FOL + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.419 0.519
## aBreastFeed_full_duration 1 0.011 0.917
## aBreastFeed_total_stopped 1 0.721 0.399
## SEX 1 0.018 0.895
## GRP 2 10.542 9.02e-05
## aSUP_FOL 1 0.660 0.419
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 13.997 0.000351
## s(FAM) 46.31 87.00 1.232 0.000126
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,include = c('aSUP_FOL'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 13.630219443 1.8204170 7.487416192
## GRPVG 4.813234906 1.1230674 4.285793270
## GRPVN 4.298596597 0.9569665 4.491898825
## SEXM 0.001631222 0.7094717 0.002299207
## aBreastFeed_full_stopped 1.476177673 2.1300517 0.693024340
## aBreastFeed_full_duration 0.015695775 0.1787616 0.087802843
## aBreastFeed_total_stopped 0.775496029 1.0694023 0.725167713
## log2_age -1.662083249 0.4696117 -3.539271521
## aSUP_FOL 1.857874466 2.2798967 0.814894141
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of folate serum level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 4.2985966 | 2.422977 | 6.174216 | 0.0000071 |
| VG vs OM | 4.8132349 | 2.612063 | 7.014407 | 0.0000182 |
| VN vs VG | -0.5146383 | -2.547852 | 1.518576 | 0.6198255 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_FOL'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_FOL'),remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_FOL'),exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_FOL'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_FOL'),
exclude = c('GRP'))
### model without supplementation
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_FOL'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.463
## Unadjusted ICC: 0.330
i = i+12.2.36 aIGF1
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aIGF1"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 113.662 27.020 4.207 7.3e-05 ***
## aBreastFeed_full_stopped 20.085 27.641 0.727 0.470
## aBreastFeed_full_duration -1.454 1.830 -0.794 0.430
## aBreastFeed_total_stopped -12.017 11.786 -1.020 0.311
## SEXM -1.821 7.339 -0.248 0.805
## GRPVG -0.817 12.225 -0.067 0.947
## GRPVN -7.920 10.230 -0.774 0.441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 3.376 4.064 26.368 < 2e-16 ***
## s(FAM) 38.932 81.000 0.985 0.00103 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.75 Deviance explained = 85%
## GCV = 1713.1 Scale est. = 1020.7 n = 122
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.528 0.470
## aBreastFeed_full_duration 1 0.631 0.430
## aBreastFeed_total_stopped 1 1.040 0.311
## SEX 1 0.062 0.805
## GRP 2 0.367 0.694
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 3.376 4.064 26.368 < 2e-16
## s(FAM) 38.932 81.000 0.985 0.00103
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.57787 0.29733 22.123 <2e-16 ***
## aBreastFeed_full_stopped 0.50552 0.29694 1.702 0.0934 .
## aBreastFeed_full_duration -0.04672 0.02357 -1.982 0.0516 .
## aBreastFeed_total_stopped -0.19417 0.15091 -1.287 0.2027
## SEXM -0.02883 0.09350 -0.308 0.7588
## GRPVG 0.02395 0.16764 0.143 0.8868
## GRPVN -0.13797 0.14000 -0.985 0.3280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.499 1.763 55.394 < 2e-16 ***
## s(FAM) 47.256 81.000 1.484 6.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.778 Deviance explained = 87.8%
## GCV = 0.28151 Scale est. = 0.15286 n = 122
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 2.898 0.0934
## aBreastFeed_full_duration 1 3.928 0.0516
## aBreastFeed_total_stopped 1 1.656 0.2027
## SEX 1 0.095 0.7588
## GRP 2 0.763 0.4705
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.499 1.763 55.394 < 2e-16
## s(FAM) 47.256 81.000 1.484 6.47e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 5.88413783 0.23765951 24.75868852
## GRPVG 0.01087846 0.17804111 0.06110086
## GRPVN -0.15782335 0.14864408 -1.06175337
## SEXM -0.03325919 0.09234161 -0.36017554
## aBreastFeed_full_stopped 0.42570075 0.27532899 1.54615300
## aBreastFeed_full_duration -0.05608753 0.02341901 -2.39495729
## aBreastFeed_total_stopped -0.25185425 0.15065679 -1.67170865
## log2_age 0.60795539 0.06439429 9.44113795
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(growth factor) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1578234 | -0.4491604 | 0.1335137 | 0.2883477 |
| VG vs OM | 0.0108785 | -0.3380757 | 0.3598326 | 0.9512789 |
| VN vs VG | -0.1687018 | -0.4893801 | 0.1519764 | 0.3024966 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
## ICC
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.513
## Unadjusted ICC: 0.228
i = i+1 2.2.37 aUr_Ca
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Ca))
column_name
## [1] "aUr_Ca"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.93548 0.96175 2.012 0.048 *
## aBreastFeed_full_stopped -0.39001 0.94523 -0.413 0.681
## aBreastFeed_full_duration 0.02591 0.06359 0.407 0.685
## aBreastFeed_total_stopped -0.25973 0.46064 -0.564 0.575
## SEXM 0.18011 0.26687 0.675 0.502
## GRPVG 0.20225 0.44385 0.456 0.650
## GRPVN -0.22058 0.39419 -0.560 0.578
## aSUP_Ca -0.42605 0.70860 -0.601 0.550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.819 3.426 2.103 0.160585
## s(FAM) 42.690 85.000 1.085 0.000526 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.47 Deviance explained = 69.6%
## GCV = 2.2338 Scale est. = 1.2699 n = 124
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.170 0.681
## aBreastFeed_full_duration 1 0.166 0.685
## aBreastFeed_total_stopped 1 0.318 0.575
## SEX 1 0.455 0.502
## GRP 2 0.605 0.549
## aSUP_Ca 1 0.362 0.550
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.819 3.426 2.103 0.160585
## s(FAM) 42.690 85.000 1.085 0.000526
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.83789 0.87322 0.960 0.341
## aBreastFeed_full_stopped -0.72358 0.84789 -0.853 0.397
## aBreastFeed_full_duration 0.05220 0.05755 0.907 0.368
## aBreastFeed_total_stopped -0.64115 0.42168 -1.520 0.133
## SEXM 0.16781 0.24288 0.691 0.492
## GRPVG 0.09157 0.42499 0.215 0.830
## GRPVN -0.35780 0.37579 -0.952 0.345
## aSUP_Ca -0.54204 0.64692 -0.838 0.405
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.593 3.14 2.140 0.135
## s(FAM) 49.494 85.00 1.505 5.13e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.559 Deviance explained = 77.1%
## GCV = 1.8447 Scale est. = 0.95081 n = 124
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 0.728 0.397
## aBreastFeed_full_duration 1 0.823 0.368
## aBreastFeed_total_stopped 1 2.312 0.133
## SEX 1 0.477 0.492
## GRP 2 0.889 0.416
## aSUP_Ca 1 0.702 0.405
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.593 3.140 2.140 0.135
## s(FAM) 49.494 85.000 1.505 5.13e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, include = c('aSUP_Ca'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.94727737 0.65176625 1.45340046
## GRPVG 0.03656867 0.48909087 0.07476867
## GRPVN -0.42404076 0.42960686 -0.98704373
## SEXM 0.21814678 0.25089877 0.86946135
## aBreastFeed_full_stopped -1.01984707 0.71840627 -1.41959657
## aBreastFeed_full_duration 0.04949519 0.06008905 0.82369737
## aBreastFeed_total_stopped -0.98089082 0.42750983 -2.29442870
## log2_age 0.29158661 0.19068875 1.52912328
## aSUP_Ca -0.77805314 0.68394924 -1.13758900
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(urinary Ca) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.4240408 | -1.2660547 | 0.4179732 | 0.3236212 |
| VG vs OM | 0.0365687 | -0.9220318 | 0.9951692 | 0.9403988 |
| VN vs VG | -0.4606094 | -1.2944359 | 0.3732171 | 0.2789448 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Ca'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Ca'), remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Ca'), exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Ca'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Ca'),
exclude = c('GRP'))
### model without other cov.
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Ca'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
i <- i+12.2.38 aCa_per_Krea
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Ca))
column_name
## [1] "aCa_per_Krea"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.253595 0.243122 5.156 2.14e-06 ***
## aBreastFeed_full_stopped -0.801508 0.231360 -3.464 0.0009 ***
## aBreastFeed_full_duration 0.020886 0.019600 1.066 0.2902
## aBreastFeed_total_stopped -0.131031 0.134074 -0.977 0.3317
## SEXM 0.004802 0.080493 0.060 0.9526
## GRPVG -0.057879 0.136382 -0.424 0.6725
## GRPVN -0.076396 0.120610 -0.633 0.5285
## aSUP_Ca 0.199352 0.218462 0.913 0.3645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1 1.192 0.27847
## s(FAM) 43.17 85 0.943 0.00407 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.539 Deviance explained = 73.1%
## GCV = 0.20892 Scale est. = 0.12102 n = 124
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca + s(log2_age) +
## s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 12.002 0.0009
## aBreastFeed_full_duration 1 1.135 0.2902
## aBreastFeed_total_stopped 1 0.955 0.3317
## SEX 1 0.004 0.9526
## GRP 2 0.203 0.8164
## aSUP_Ca 1 0.833 0.3645
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00 1.00 1.192 0.27847
## s(FAM) 43.17 85.00 0.943 0.00407
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_total_stopped +
SEX +
GRP +
aSUP_Ca +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.40482 0.43536 -3.227 0.00186 **
## aBreastFeed_total_stopped -0.62344 0.41161 -1.515 0.13411
## SEXM -0.01042 0.24324 -0.043 0.96595
## GRPVG 0.28534 0.39479 0.723 0.47210
## GRPVN 0.02790 0.35445 0.079 0.93746
## aSUP_Ca 0.40476 0.65072 0.622 0.53584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.118 2.546 1.837 0.172038
## s(FAM) 41.584 85.000 1.087 0.000279 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.506 Deviance explained = 70.2%
## GCV = 1.8452 Scale est. = 1.1056 n = 124
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_total_stopped + SEX + GRP + aSUP_Ca +
## s(log2_age) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_total_stopped 1 2.294 0.134
## SEX 1 0.002 0.966
## GRP 2 0.347 0.708
## aSUP_Ca 1 0.387 0.536
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.118 2.546 1.837 0.172038
## s(FAM) 41.584 85.000 1.087 0.000279
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, include = c('aSUP_Ca'), remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -0.076536500 0.66336003 -0.115377014
## GRPVG 0.190229336 0.43305270 0.439275258
## GRPVN 0.024865529 0.38476039 0.064626010
## SEXM -0.001049997 0.26840706 -0.003911958
## aBreastFeed_full_stopped -1.442484124 0.77340047 -1.865119285
## aBreastFeed_full_duration 0.025411915 0.06572311 0.386651151
## aBreastFeed_total_stopped -0.646864841 0.44424712 -1.456092382
## log2_age -0.063253980 0.19856893 -0.318549237
## aSUP_Ca 0.557720554 0.72542534 0.768818683
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(uCa:uCreatinie ratio) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0248655 | -0.7292510 | 0.778982 | 0.9484718 |
| VG vs OM | 0.1902293 | -0.6585384 | 1.038997 | 0.6604621 |
| VN vs VG | -0.1653638 | -0.9134796 | 0.582752 | 0.6648468 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(include = c('aSUP_Ca'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(include = c('aSUP_Ca'), remove_random = TRUE)
### model without sex
mod_nsex <- rlme(include = c('aSUP_Ca'), exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(include = c('aSUP_Ca'),
exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(include = c('aSUP_Ca'),
exclude = c('GRP'))
### model without other cov.
mod_other_cov <- rlme()
### model without log2_age
mod_log2_age <- rlme(include = c('aSUP_Ca'),
exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
i <- i+12.2.39 aI_per_Krea
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aI_per_Krea"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1641.80 378.31 4.340 3.34e-05 ***
## aBreastFeed_full_stopped -1073.17 379.75 -2.826 0.00566 **
## aBreastFeed_full_duration -1.69 34.15 -0.049 0.96062
## aBreastFeed_total_stopped 20.00 215.44 0.093 0.92623
## SEXM 51.35 133.75 0.384 0.70181
## GRPVG -138.59 184.42 -0.751 0.45407
## GRPVN -191.27 167.39 -1.143 0.25585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00e+00 1 0.337 0.563
## s(FAM) 3.94e-09 77 0.000 0.534
##
## R-sq.(adj) = 0.134 Deviance explained = 18.9%
## GCV = 5.2302e+05 Scale est. = 4.8533e+05 n = 111
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 7.986 0.00566
## aBreastFeed_full_duration 1 0.002 0.96062
## aBreastFeed_total_stopped 1 0.009 0.92623
## SEX 1 0.147 0.70181
## GRP 2 0.665 0.51651
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.00e+00 1.00e+00 0.337 0.563
## s(FAM) 3.94e-09 7.70e+01 0.000 0.534
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.06092 0.68073 14.780 <2e-16 ***
## aBreastFeed_full_stopped -1.60706 0.67791 -2.371 0.0198 *
## aBreastFeed_full_duration 0.03002 0.05653 0.531 0.5966
## aBreastFeed_total_stopped -0.06920 0.36187 -0.191 0.8488
## SEXM 0.12638 0.22277 0.567 0.5718
## GRPVG -0.33993 0.31632 -1.075 0.2853
## GRPVN -0.46948 0.28494 -1.648 0.1027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.434 1.752 1.414 0.155
## s(FAM) 7.689 75.000 0.117 0.267
##
## R-sq.(adj) = 0.253 Deviance explained = 35.6%
## GCV = 1.4416 Scale est. = 1.2322 n = 111
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 5.620 0.0198
## aBreastFeed_full_duration 1 0.282 0.5966
## aBreastFeed_total_stopped 1 0.037 0.8488
## SEX 1 0.322 0.5718
## GRP 2 1.378 0.2571
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 1.434 1.752 1.414 0.155
## s(FAM) 7.689 75.000 0.117 0.267
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 10.30038788 0.52224027 19.72346533
## GRPVG -0.30623789 0.31005589 -0.98768609
## GRPVN -0.37843672 0.28143687 -1.34465937
## SEXM 0.11919881 0.22487136 0.53007556
## aBreastFeed_full_stopped -1.51688347 0.63846764 -2.37581888
## aBreastFeed_full_duration 0.03624065 0.05741881 0.63116326
## aBreastFeed_total_stopped -0.02027727 0.36221975 -0.05598059
## log2_age -0.34291254 0.16105193 -2.12920475
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of log2(uIodine:uCreatinine ratio) level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.3784367 | -0.9300429 | 0.1731694 | 0.1787353 |
| VG vs OM | -0.3062379 | -0.9139363 | 0.3014605 | 0.3233064 |
| VN vs VG | -0.0721988 | -0.6283935 | 0.4839958 | 0.7991710 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)
i <- i+12.2.40 aP_per_Krea
Data selection and diagnostic plots
Open code
column_name <- names(dat_child_all)[i+ni]
dat_mod <- dat_child_all %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aP_per_Krea"
gamm <- gam(
outcome ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.925357 1.132349 1.700 0.093669 .
## aBreastFeed_full_stopped 1.881968 1.111186 1.694 0.094935 .
## aBreastFeed_full_duration 0.001132 0.073961 0.015 0.987830
## aBreastFeed_total_stopped 1.133605 0.540213 2.098 0.039606 *
## SEXM 0.064015 0.310388 0.206 0.837223
## GRPVG -0.632002 0.530655 -1.191 0.237826
## GRPVN -1.901702 0.467373 -4.069 0.000126 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.924 3.535 7.237 0.000129 ***
## s(FAM) 46.491 85.000 1.238 0.000293 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.64 Deviance explained = 80.2%
## GCV = 3.0103 Scale est. = 1.6407 n = 124
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 2.868 0.09493
## aBreastFeed_full_duration 1 0.000 0.98783
## aBreastFeed_total_stopped 1 4.403 0.03961
## SEX 1 0.043 0.83722
## GRP 2 9.258 0.00028
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.924 3.535 7.237 0.000129
## s(FAM) 46.491 85.000 1.238 0.000293
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
aBreastFeed_full_stopped +
aBreastFeed_full_duration +
aBreastFeed_total_stopped +
SEX +
GRP +
s(log2_age) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.640566 0.536544 1.194 0.2365
## aBreastFeed_full_stopped 1.232943 0.528545 2.333 0.0225 *
## aBreastFeed_full_duration -0.007906 0.035387 -0.223 0.8238
## aBreastFeed_total_stopped 0.341739 0.255868 1.336 0.1859
## SEXM -0.010679 0.147962 -0.072 0.9427
## GRPVG -0.246866 0.246555 -1.001 0.3201
## GRPVN -0.939780 0.217520 -4.320 4.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.897 3.518 7.033 0.000160 ***
## s(FAM) 42.828 85.000 1.041 0.000937 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.631 Deviance explained = 78.6%
## GCV = 0.68555 Scale est. = 0.39406 n = 124
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ aBreastFeed_full_stopped + aBreastFeed_full_duration +
## aBreastFeed_total_stopped + SEX + GRP + s(log2_age) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## aBreastFeed_full_stopped 1 5.442 0.0225
## aBreastFeed_full_duration 1 0.050 0.8238
## aBreastFeed_total_stopped 1 1.784 0.1859
## SEX 1 0.005 0.9427
## GRP 2 10.949 7.14e-05
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(log2_age) 2.897 3.518 7.033 0.000160
## s(FAM) 42.828 85.000 1.041 0.000937
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit. We choose is original scale
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE, remove_random = FALSE),
path = paste0('gitignore/run/mod_child_all_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.28639520 0.78634131 1.63592472
## GRPVG -0.63276234 0.45099825 -1.40302614
## GRPVN -2.11710046 0.40853656 -5.18215665
## SEXM 0.11351050 0.32533097 0.34890777
## aBreastFeed_full_stopped 4.14024556 0.95401253 4.33982306
## aBreastFeed_full_duration -0.00623018 0.08080801 -0.07709854
## aBreastFeed_total_stopped 1.54665232 0.53104555 2.91246641
## log2_age -1.18244142 0.22876662 -5.16876722
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'Results of linear mixed-effects model of `uP:uCreatinine ratio` level. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -2.1171005 | -2.917817 | -1.3163835 | 0.0000002 |
| VG vs OM | -0.6327623 | -1.516703 | 0.2511780 | 0.1606090 |
| VN vs VG | -1.4843381 | -2.287226 | -0.6814507 | 0.0002907 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme()
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(remove_random = TRUE)
### model without sex
mod_nsex <- rlme(exclude = c('SEX'))
### model without breastfeeding-related predictors
mod_nbf <- rlme(exclude = c('aBreastFeed_full_stopped',
'aBreastFeed_total_stopped',
'aBreastFeed_full_duration'))
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'))
### model without other cov.
mod_other_cov <- NA
### model without log2_age
mod_log2_age <- rlme(exclude = c('log2_age'))Putting key results together
Open code
## AIC
AIC_child_all <- add_AIC(AIC_child_all, mixef = TRUE)
diet_child_all <- add_eff(diet_child_all, res, mixef = TRUE); diet_child_all_non_robust <- add_eff(diet_child_all_non_robust, res2, mixef = TRUE)2.2.41 Save tables
Open code
## Between-diets differences
if(file.exists('gitignore/data/diet_child_all_mixeff.xlsx') == FALSE){
diet_child_all <- diet_child_all[-1,]
diet_child_all$VN_OM_P_adj <- p.adjust(diet_child_all$VN_OM_P, method = 'fdr')
diet_child_all$VG_OM_P_adj <- p.adjust(diet_child_all$VG_OM_P, method = 'fdr')
diet_child_all$VN_VG_P_adj <- p.adjust(diet_child_all$VN_VG_P, method = 'fdr')
# for (i in seq(1, nrow(diet_child_all))) {
# padj <- p.adjust(c(
# diet_child_all$VN_OM_P[i],
# diet_child_all$VG_OM_P[i],
# diet_child_all$VN_VG_P[i]),
# method = 'hochberg')
#
# diet_child_all$VN_OM_P_adj[i] <- padj[1]
# diet_child_all$VG_OM_P_adj[i] <- padj[2]
# diet_child_all$VN_VG_P_adj[i] <- padj[3]
# }
diet_child_all <- diet_child_all %>%
select(outcome, estimand,
VN_OM_diff, VN_OM_P, VN_OM_P_adj,
VG_OM_diff, VG_OM_P, VG_OM_P_adj,
VN_VG_diff, VN_VG_P, VN_VG_P_adj) %>%
mutate(across(.cols = 3:11, .fns = ~as.numeric(as.character(.))))
write.xlsx(diet_child_all, 'gitignore/data/diet_child_all_mixeff.xlsx')
}
if(file.exists('gitignore/data/diet_child_all_mixeff_non_robust.xlsx') == FALSE){
diet_child_all_non_robust <- diet_child_all_non_robust[-1,]
diet_child_all_non_robust$VN_OM_P_adj <- p.adjust(
diet_child_all_non_robust$VN_OM_P, method = 'fdr')
diet_child_all_non_robust$VG_OM_P_adj <- p.adjust(
diet_child_all_non_robust$VG_OM_P, method = 'fdr')
diet_child_all_non_robust$VN_VG_P_adj <- p.adjust(
diet_child_all_non_robust$VN_VG_P, method = 'fdr')
# for (i in seq(1, nrow(diet_child_all_non_robust))) {
# padj <- p.adjust(c(
# diet_child_all_non_robust$VN_OM_P[i],
# diet_child_all_non_robust$VG_OM_P[i],
# diet_child_all_non_robust$VN_VG_P[i]),
# method = 'hochberg')
#
# diet_child_all_non_robust$VN_OM_P_adj[i] <- padj[1]
# diet_child_all_non_robust$VG_OM_P_adj[i] <- padj[2]
# diet_child_all_non_robust$VN_VG_P_adj[i] <- padj[3]
# }
diet_child_all_non_robust <- diet_child_all_non_robust %>%
select(outcome, estimand,
VN_OM_diff, VN_OM_P, VN_OM_P_adj,
VG_OM_diff, VG_OM_P, VG_OM_P_adj,
VN_VG_diff, VN_VG_P, VN_VG_P_adj) %>%
mutate(across(.cols = 3:11, .fns = ~as.numeric(as.character(.))))
write.xlsx(diet_child_all_non_robust, 'gitignore/data/diet_child_all_mixeff_non_robust.xlsx')
}
## Importance of predictors
if(file.exists('gitignore/data/AIC_child_all_mixeff.xlsx') == FALSE){
AIC_child_all <- AIC_child_all[-1,]
write.xlsx(AIC_child_all, 'gitignore/data/AIC_child_all_mixeff.xlsx')
}3 Reproducibility
Open code
sessionInfo()
## R version 4.4.3 (2025-02-28)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=cs_CZ.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=cs_CZ.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=cs_CZ.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=cs_CZ.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Prague
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] mice_3.17.0 patchwork_1.2.0 ggrepel_0.9.5 robustlmm_3.3-1
## [5] gridExtra_2.3 pheatmap_1.0.12 performance_0.12.2 quantreg_5.98
## [9] SparseM_1.81 bayesplot_1.8.1 ggdist_3.3.2 kableExtra_1.4.0
## [13] lubridate_1.8.0 corrplot_0.92 arm_1.12-2 MASS_7.3-64
## [17] projpred_2.0.2 glmnet_4.1-8 boot_1.3-31 cowplot_1.1.1
## [21] pROC_1.18.0 mgcv_1.9-1 nlme_3.1-167 openxlsx_4.2.5
## [25] flextable_0.9.6 sjPlot_2.8.16 car_3.1-2 carData_3.0-5
## [29] gtsummary_2.0.2 emmeans_1.10.4 ggpubr_0.4.0 lme4_1.1-35.5
## [33] Matrix_1.7-0 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
## [37] purrr_1.0.2 readr_2.1.2 tidyr_1.3.1 tibble_3.2.1
## [41] ggplot2_3.5.1 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] splines_4.4.3 later_1.3.0 gamm4_0.2-6
## [4] cellranger_1.1.0 datawizard_0.12.2 rpart_4.1.24
## [7] reprex_2.0.1 lifecycle_1.0.4 rstatix_0.7.0
## [10] lattice_0.22-5 insight_0.20.2 backports_1.5.0
## [13] magrittr_2.0.3 rmarkdown_2.27 yaml_2.3.5
## [16] httpuv_1.6.5 zip_2.2.0 askpass_1.1
## [19] DBI_1.1.2 minqa_1.2.4 RColorBrewer_1.1-2
## [22] multcomp_1.4-18 abind_1.4-5 rvest_1.0.2
## [25] nnet_7.3-20 TH.data_1.1-0 sandwich_3.0-1
## [28] gdtools_0.3.7 pbkrtest_0.5.1 crul_1.5.0
## [31] MatrixModels_0.5-3 svglite_2.1.3 codetools_0.2-19
## [34] xml2_1.3.3 tidyselect_1.2.1 shape_1.4.6
## [37] farver_2.1.0 ggeffects_1.7.0 httpcode_0.3.0
## [40] matrixStats_1.3.0 jsonlite_1.8.8 mitml_0.4-3
## [43] ellipsis_0.3.2 ggridges_0.5.3 survival_3.7-0
## [46] iterators_1.0.14 systemfonts_1.0.4 foreach_1.5.2
## [49] tools_4.4.3 ragg_1.2.1 Rcpp_1.0.13
## [52] glue_1.7.0 pan_1.6 xfun_0.46
## [55] distributional_0.4.0 loo_2.4.1 withr_3.0.1
## [58] fastmap_1.2.0 fansi_1.0.6 openssl_1.4.6
## [61] digest_0.6.37 R6_2.5.1 mime_0.12
## [64] estimability_1.5.1 textshaping_0.3.6 colorspace_2.0-2
## [67] utf8_1.2.4 generics_0.1.3 fontLiberation_0.1.0
## [70] data.table_1.15.4 robustbase_0.93-9 httr_1.4.2
## [73] htmlwidgets_1.6.4 pkgconfig_2.0.3 gtable_0.3.0
## [76] htmltools_0.5.8.1 fontBitstreamVera_0.1.1 scales_1.3.0
## [79] knitr_1.48 rstudioapi_0.16.0 tzdb_0.2.0
## [82] uuid_1.0-3 coda_0.19-4 curl_4.3.2
## [85] nloptr_2.0.0 zoo_1.8-9 sjlabelled_1.2.0
## [88] parallel_4.4.3 pillar_1.9.0 vctrs_0.6.5
## [91] promises_1.2.0.1 jomo_2.7-3 dbplyr_2.1.1
## [94] xtable_1.8-4 evaluate_1.0.0 fastGHQuad_1.0.1
## [97] mvtnorm_1.1-3 cli_3.6.3 compiler_4.4.3
## [100] rlang_1.1.4 crayon_1.5.0 rstantools_2.1.1
## [103] ggsignif_0.6.3 labeling_0.4.2 modelr_0.1.8
## [106] plyr_1.8.6 sjmisc_2.8.10 fs_1.6.4
## [109] stringi_1.7.6 viridisLite_0.4.0 assertthat_0.2.1
## [112] munsell_0.5.0 fontquiver_0.2.1 sjstats_0.19.0
## [115] hms_1.1.1 gfonts_0.2.0 shiny_1.9.1
## [118] highr_0.11 haven_2.4.3 broom_1.0.6
## [121] DEoptimR_1.0-10 readxl_1.3.1 officer_0.6.6